TROLL PATROL INDIA Exposing Online Abuse Faced by Women Politicians in India - Amnesty International India

 
TROLL PATROL INDIA Exposing Online Abuse Faced by Women Politicians in India - Amnesty International India
TROLL PATROL INDIA
Exposing Online Abuse Faced by Women
Politicians in India
TROLL PATROL INDIA Exposing Online Abuse Faced by Women Politicians in India - Amnesty International India
Amnesty International India is part of the Amnesty International global human rights movement.
         Amnesty International India seeks to protect and promote the human rights of everyone in India.
         Our vision is for every person in India to enjoy all the rights enshrined in the Universal Declaration
         of Human Rights, other international human rights standards and the Constitution of India. We are
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First published in 2020 by
Indians For Amnesty International Trust
#235, Ground Floor, 13th Cross, Indira Nagar, 2nd Stage,
Bengaluru – 560038, Karnataka, India

© Indians For Amnesty International Trust (Amnesty International India)
Original language: English

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TROLL PATROL INDIA Exposing Online Abuse Faced by Women Politicians in India - Amnesty International India
TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA 3

TROLL PATROL INDIA
Exposing Online Abuse Faced by Women
Politicians in India

CONTENTS

EXECUTIVE SUMMARY                                                                   04 - 07

METHODOLOGY                                                                         08 - 15

DECODERS: THE SPIRIT OF VOLUNTEERISM ENABLED
                                                                                    16 - 19
THE CROWDSOURCED RESEARCH

FINDINGS                                                                            20 - 40

TWITTER: HUMAN RIGHTS RESPONSIBILITIES                                              41 - 47

TROLL PATROL INDIA: RECOMMENDATIONS                                                 48 - 49

ANNEXURE 1: ENRICHMENT                                                              50 - 51

ANNEXURE 2: LANGUAGES, LANGUAGE DETECTION AND
                                                                                    52 - 54
            FLAGGING

ANNEXURE 3: AMNESTY DECODERS TOOL AND
                                                                                    55 - 56
            SCREENSHOTS

ANNEXURE 4: WEIGHTING                                                                       57

ANNEXURE 5: AGREEMENT ANALYSIS                                                      58 - 60

ANNEXURE 6: LETTER TO TWITTER                                                       61 - 64

ANNEXURE 7: TWITTER'S RESPONSE                                                      65 - 76
TROLL PATROL INDIA Exposing Online Abuse Faced by Women Politicians in India - Amnesty International India
4 TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA

Executive Summary
TROLL PATROL INDIA Exposing Online Abuse Faced by Women Politicians in India - Amnesty International India
TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA 5

India is one of the largest and fastest growing audience
markets globally for Twitter, a social media platform.1 Touted                                         PROBLEMATIC CONTENT
as a ‘safe place for free expression’, Twitter was envisioned
                                                                                                       Tweets that contain hurtful or hostile content, especially if
to be a space where marginalised populations, including
                                                                                                       repeated to an individual on multiple occasions, but do not
women, Dalits and religious minorities, would have an equal                                            necessarily meet the threshold of abuse. Problematic tweets
opportunity to make their voices heard. Over the years, the                                            can reinforce negative or harmful stereotypes against a group
social media platform has evolved into an indispensable tool                                           of individuals (e.g. negative stereotypes about a race or
for political engagement, campaigning and activism, but has                                            people who follow a certain religion).
the vision translated into reality? Many women do not believe                                          We believe that such tweets may still have the effect of
so. Every day, women on Twitter face a barrage of abuse: from                                          silencing an individual or groups of individuals. However, we
racist and sexist attacks to rape and death threats.                                                   do acknowledge that problematic tweets may be protected
                                                                                                       expression and would not necessarily be subject to removal
Using crowd-sourced research and data science, Indians for                                             from the platform.
Amnesty International Trust,2 in collaboration with Amnesty
International – International Secretariat (AI-IS) measured the
scale and nature of online abuse faced by women politicians
in India during the 2019 General Elections of India. The
study found that abuse experienced by Indian women                                                     ABUSIVE CONTENT
politicians was high, suggesting that Twitter is failing in its                                        Tweets that promote violence against or threaten people
responsibility to respect women’s rights online. The study                                             based on their race, ethnicity, national origin, sexual
supports the notion that for many women, the social media                                              orientation, gender, gender identity, religious affiliation, age,
platform has turned into a ‘battlefield’.3                                                             disability, or serious disease. Examples include physical
                                                                                                       or sexual threats, wishes for the physical harm or death,
We studied tweets mentioning 95 Indian women politicians in                                            reference to violent events, behaviour that incites fear or
the three-month period of March – May 2019, in the lead-                                               repeated slurs, epithets, racist and sexist tropes, or other
up to, during and shortly after the 2019 General Elections                                             content that degrades someone.
in India. Of the total volume of 7 million tweets mentioning
these politicians, we sampled 114,716 for the purpose of
analysis through our Troll Patrol India project.4

Engaging 1,912 volunteers, known as ‘Decoders’ from 82
                                                                                                 “When you are on social media,
countries, the tweets were analysed to create a labelled set of
'problematic' or 'abusive' content. The Decoders were shown
                                                                                                 you face trolls, threats, abuses
a tweet with username obscured, mentioning one of the
women in our study. They were, then asked simple questions
                                                                                                 and challenges 100% of the
                                                                                                 time. Their purpose is to silence
about whether the tweet was problematic or abusive, and if
so, whether they revealed sexist or misogynistic, religious,
casteist, racist or homophobic abuse, or physical or sexual
threats. Each tweet was analysed by multiple people. The                                         you. It makes you want to cry.
Decoders were given a tutorial, definitions and examples
of 'problematic' and 'abusive' content, as well as an online                                     They talk about your personal
forum where they could discuss the tweets with each other
and with our researchers. The labelling of the Decoders was
analysed between July 2019 - November 2019.5
                                                                                                 life, your looks, and your family.”
                                                                                                            – Alka Lamba, Member, Indian National Congress

1.   Colin Crowell, Setting the record straight on Twitter India and impartiality, 8 Feb. 2019, Twitter, https://blog.twitter.com/en_in/topics/events/2019/impartiality.html
2.   Hereinafter referred as ‘Amnesty International India’
3.   Interview with Shazia Ilmi, Member, Bharatiya Janata Party, on 25 November 2019 in New Delhi, India
4.   For purpose of study, the term ‘women politicians’ includes party members who may or may not have held elected office
5.   The crowd-sourced research mobilised as many as 1,912 Decoders from 82 countries, with 57.3% (1,095 Decoders) hailing from India across 26 states. About 1,750+ hours was
     contributed cumulatively by the Decoders. The research analysed tweets in 9 languages, including Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Tamil and Telugu.
TROLL PATROL INDIA Exposing Online Abuse Faced by Women Politicians in India - Amnesty International India
6 TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA

WHAT DID WE FIND?                                                                       suggested categories. Nevertheless, of all the tweets
                                                                                        that were labelled as problematic or abusive, over 1 in 5
                                                                                        answers (19.9%) showed sexism or misogyny.

1.    1 in every 7 tweets that mentioned women politicians in                           A deeper look at the tweets that demonstrated sexism
      India was 'problematic' or 'abusive'                                              or misogyny showed that sexism was experienced by
                                                                                        women independent of political ideology or affiliation,
      13.8% of the tweets that mentioned 95 women
                                                                                        religion, caste, race, age, marital status, and election
      politicians in the study were problematic (10.5%) or
                                                                                        outcome.
      abusive (3.3%). This amounted to 1 million problematic
      or abusive mentions of the 95 women between March
      and May 2019, or over 10,000 problematic or abusive
      tweets every day across all women in the sample.                             5.   Muslim women received 94.1% more ethnic or religious
                                                                                        slurs than women from other religions

                                                                                        Women politicians who are or perceived as Muslims
2.    Indian women politicians experienced substantially                                received significantly more abuse when compared to
      higher abuse than their UK and USA counterparts                                   women from other religions. They received 55.5% more
                                                                                        problematic or abusive content. 26.4% of the problematic
      A similar study conducted by Amnesty International in
                                                                                        or abusive content experienced by them contained ethnic/
      the UK and USA in 2018 to measure the online abuse
                                                                                        religious slurs, nearly double the proportion for women who
      faced by 323 women politicians found that 7.1% tweets
                                                                                        are or perceived as Hindus (13.7%).
      mentioning politicians were 'problematic' or 'abusive'.6
      Whereas this study, using a similar methodology, but
      focusing on a shorter period during elections found
      that Indian women politicians experienced 13.8%                              6.   Women politicians belonging to marginalised castes
      problematic or abusive tweets, which is substantially                             received 59% more caste-based abuse compared to
      higher.                                                                           women from other castes

                                                                                        Women from marginalised caste received 59% more
                                                                                        caste based abuse than women from general castes.
3.    Women politicians prominent on Twitter are targeted more                          In cases, where problematic or abusive content was
                                                                                        identified, women belonging to marginalised castes,
      It has long been assumed that being more visible on
                                                                                        such as Scheduled Castes, Scheduled Tribes and
      social media as a woman, can lead to more abuse. This
                                                                                        Other Backward Classes (8.6%) received more caste-
      study confirms that there is a clear correlation between
                                                                                        based slurs than those belonging to general (5.4%) or
      the number of mentions and the proportion of abusive
                                                                                        Unknown/ Undeclared caste (7.2%).7
      content received by women.
                                                                                        Notably, a prominent woman politician from
      We tested this by grouping the politicians by mentions.
                                                                                        marginalised caste received significantly more caste-
      When we split the group into the top 10 most-mentioned
                                                                                        based slurs than others. This indicates that caste
      politicians and others, we found that the top 10 had a
                                                                                        identity is more often than not, a key element of
      mean of 14.8% problematic or abusive content versus
                                                                                        problematic or abusive content for women belonging to
      10.8% for the others. This meant that while the top 10
                                                                                        marginalised castes.
      received 74.1% of all mentions, they received 79.9% of
      problematic or abusive mentions.

                                                                                   7.   Women politicians from political parties other than the
                                                                                        Bharatiya Janata Party experienced more abuse
4.    1 in every 5 problematic or abusive tweets was sexist or
      misogynistic                                                                      While women politicians across all political parties
                                                                                        experienced sexist abuse, their overall experience was
      Decoders were directed to label the problematic or
                                                                                        divided along party lines.
      abusive content as containing sexism and/or misogyny,
      ethnic or religious slur, racism, casteism, homophobia or                         As compared to women politicians associated with the
      transphobia, sexual threats, physical threats or ‘other’.                         Bharatiya Janata Party (BJP), which is also the ruling
      'Other' was used when tweets containing problematic or                            party in India currently, women politicians from ‘other
      abusive content did not fit in the categories provided.                           parties’ 8 experienced 56.7% more problematic or
      Notably, the most common selection was the 'other'                                abusive content. Women politicians associated with the
      category (74.1%). This indicated that most problematic                            Indian National Congress (INC) also received 45.3%
      or abusive content did not fit neatly into any of the                             more problematic or more abusive content than BJP.
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TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA 7

Online abuse against women on this scale should not and                                              uniform, and are based on human rights standards and
does not have to exist on social media platforms. Companies                                          gender-sensitive due diligence. Considering India’s linguistic
like Twitter have a responsibility to respect human rights,                                          diversity, Twitter should ensure coverage not only of India’s
which means ensuring that women and marginalised groups                                              main languages, but also regional languages, with due
using the platform are able to express themselves freely and                                         focus on mixed language tweets where native scripts are
without discrimination.                                                                              used alongside Latin scripts. Further, it should ensure that
                                                                                                     discrimination and abuse on the basis of gender, caste,
In the recent past, Twitter has admitted that it has created
                                                                                                     religion, ethnicity, gender identity, sexual orientation
an unsafe space for women by perpetuating harassment and
                                                                                                     and other identifying factors does not prevent users from
abuse. While Twitter has guidelines in place to identify abuse
                                                                                                     exercising their right to freedom of expression equally on the
and hate, and has also improved its policies and reporting
                                                                                                     platform. Importantly, it must constantly and transparently
process over the years, the findings suggest that these
                                                                                                     evaluate and measure whether it is effectively tackling online
policies are not sufficient to address the toxicity that women
                                                                                                     violence against women.
face online.
                                                                                                     Online abuse has the power to belittle, demean, intimidate
Amnesty International through its Toxic Twitter study (2018)9
                                                                                                     and eventually silence women. Twitter must reaffirm its
and Troll Patrol study (2018)10 has repeatedly asked Twitter
                                                                                                     commitment to providing a ‘safe space’ to women and
to make available meaningful and comprehensive data
                                                                                                     marginalised communities. Until then, the silencing effect
regarding the scale and nature of abuse on their platform, as
                                                                                                     of abuse on the platform will continue to stand in the way of
well as how they are addressing it.
                                                                                                     women’s right to expression and equality.
Based on the findings of this study, we recommend further
steps for Twitter to ensure that its policies are transparent,

                “People should know what women in politics endure,
                what they have to put up with and how unequal it
                becomes for them. It is such a tough battlefield, so to
                speak. Really I do believe that Twitter is my workplace.”
                “But if my workplace were to be a battlefield, all the
                time, would I be able to contribute, to the cause that I
                represent, easily and with fairness, if I am constantly
                being attacked for being a woman.”
                                                                                                    – Shazia Ilmi, Member, Bharatiya Janata Party

6.    Amnesty International, Troll Patrol Findings, https://decoders.amnesty.org/projects/troll-patrol/findings
7.    Marginalised caste, in this study, included politicians belonging to Scheduled Caste, Scheduled Tribe and Other Backward castes. The Scheduled Caste (SCs) and Scheduled Tribes (STs) are
      officially designated groups of historically disadvantaged people in India. Articles 341 and 342 of the Constitution of India define who would be Scheduled Castes and Scheduled Tribes with
      respect to any State or Union Territory. For the purpose of study, the caste identity of the women was taken from Lok Dhabha, an initiative of Ashoka University. ‘Unknown/ Undeclared caste’
      refers to those who either did not publicly declare their caste and/or Amnesty International India was unable to identify them with a particular caste group.
8.    Other parties included Aam Aadmi Party, Apna Dal, All India Anna Dravida Munnetra Kazhagam, All India Trinamool Congress, Bahujan Samaj Party, Community Party of India
      (Marxist), Communist Party of India (Marxist-Leninist), Dravida Munnetra Kazhagam, Jammu & Kashmir National Congress, Jammu & Kashmir Peoples Democratic Party, Jharkhand
      Mukti Morcha, Rashtriya Janata Dal, Shiromani Akali Dal, Shiv Sena, Samajwadi Party, Telangana Rashtra Samiti, Yuvajana Sramika Rythu Congress Party. Our analysis could not be
      further disaggregated by ‘other’ parties as we did not have statistically relevant samples for each party (having a sample per party between 1 and 4 members). Further, we observed
      that, except for some notable cases, many women from these parties did not have an active Twitter account.
9.    Amnesty International, Toxic Twitter, INDEX NO. ACT 30/8070//2018
10.   Amnesty International, Troll Patrol Findings, https://decoders.amnesty.org/projects/troll-patrol/findings
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Methodology
Troll Patrol India project is a joint effort by human rights researchers, technical experts and thousands
of online volunteers to build a large crowdsourced dataset of online abuse against women politicians in
India.

This chapter explores the methodology adopted to measure the scale and nature of online abuse faced
by women politicians in India. It discusses ‘who did we study’, ‘when did we study’ and ‘how did we
study’.

The women politicians represented a variety of political views spanning the ideological spectrum. Using
data science we were able to provide a quantitative analysis of the scale of online abuse against women
politicians in India.
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TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA 9

Troll Patrol India is a crowdsourcing effort to demonstrate                                        The Troll Patrol India microsite was launched for decoding
the scale and nature of online abuse against Indian women                                          on 15 May 2019 and was open till 8 August 2019. Great
politicians on Twitter in the context of the 2019 General                                          effort was put into designing the user-friendly and interactive
Indian Elections of India. Following the methodology                                               interface, and could be accessed by Decoders through
developed together with Element AI for Amnesty                                                     computers and mobile phones.13
International’s 2018 Troll Patrol study, we built a database of
over 114,716 tweets mentioning 95 women politicians from

                                                                                                   STUDIED POPULATION: WOMEN
India and asked digital volunteers, known as ‘Decoders’ to
identify problematic and abusive content in those tweets.

An impressive number of 1,912 Decoders from 82                                                     POLITICIANS ON TWITTER
countries analysed the tweets to create a labelled dataset of
problematic or abusive content. The Decoders were shown a
tweet with username obscured, mentioning one of the women                                          IDENTIFYING INDIAN WOMEN
in our study, then were asked simple questions about whether
the tweets were problematic or abusive, and if so, whether                                         POLITICIANS
they revealed misogynistic, casteist or racist abuse, or other
types of violent threats. Each tweet was analysed by multiple                                      The sample of women politicians was identified prior to the
Decoders.                                                                                          submission of nomination papers by the women contesting in
                                                                                                   the 2019 General Elections. The criteria for sample selection
The Decoders were shown a video tutorial and definitions                                           were as follows-
and examples of problematic and abusive content, and they
were encouraged to engage on an online forum where they                                            •      Members of Parliament in the two most recently elected
could discuss the tweets with each other and with Amnesty                                                 Lower House of Parliament (15th and 16th Lok Sabha)
International’s and Amnesty International India's researchers.
                                                                                                   •      Members of Parliament in the two most recently elected
In total the Decoders labelled 142,474 tweets, totalling                                                  Upper House of Parliament (Rajya Sabha)
474,383 individual answers. This large number allowed us
                                                                                                   •      Members of the Legislative Assembly of the States and
to have both a dataset of 114,716 tweets sampled uniformly
                                                                                                          Union Territories as of February 2019
at random, and a smaller dataset of 27,758 tweets sampled
from a more experimental procedure. The experimental                                               •      Party office bearers and spokeswomen for all the
dataset was made possible by the huge amount of interest in                                               national and regional political parties
the campaign. It was a learning opportunity for us to further
our experience of using advanced analysis tools on real world                                      •      Members from reserved constituencies (seats reserved
data, and preparing the terrain for future campaigns. In the                                              for specific groups in the Indian Parliament at national
interest of simplicity and methodological clarity, however, all                                           and state level)
the statistics in the rest of this report were computed on the                                     •      Current and former chief ministers (elected heads of
basis of the tweets sampled uniformly at random.                                                          government) for all states and union territories
Using the subset of 114,716 tweets sampled uniformly at                                            The Twitter accounts of these women were identified. If the
random and annotated by the Decoders, we extrapolated the                                          account was not “verified” (a Twitter badge that recognises
abuse analysis to the full 7 million tweets that mentioned the                                     the account to be authentic)14 then the team investigated to
Indian women politicians selected for our study. The results                                       ensure that the account was genuine, by using party websites
published in this study are based on this.                                                         and other such sources. Our research resulted in 101 Twitter
The tweets were deployed through Troll Patrol India page11                                         handles. Of these some had very little Twitter activity – 95
- hosted on Amnesty Decoders,12 the micro-tasking platform                                         politicians had at least one tweet mention in the final dataset
based on Hive (Labs, 2014) and Discourse (Discourse). This                                         and 82 had 10 or more tweet mentions in the sample set
platform by Amnesty International engages Decoders (mostly                                         given to Decoders.
existing members and supporters) in human rights research.

11.   Troll Patrol India, Amnesty International India, https://decoders.amnesty.org/projects/troll-patrol-india
12.   Troll Patrol, Amnesty International, https://decoders.amnesty.org/projects/troll-patrol
13.   See, Annexure 3, Amnesty Decoders Tool and Screenshots
14.   Note that Twitter verification has been mostly on hold since 2018. See, About Verified Accounts, Twitter, https://help.twitter.com/en/managing-your-account/about-twitter-verified-
      accounts
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PROFILE OF THE POLITICIANS                                                          142,474 tweets for the Troll Patrol India project. The
                                                                                    sample that was labelled by Decoders included 114,716
                                                                                    tweets sampled uniformly at random, and a smaller dataset
We researched characteristics of these politicians such
                                                                                    of 27,758 tweets sampled from a more experimental
as party affiliation, political inclination, their official role
                                                                                    procedure.15 All the statistics in the rest of this study were
prior to the elections, current post, whether they contested
                                                                                    computed on the basis of the 114,716 tweets sampled
the election, whether they won the election, their religion,
                                                                                    uniformly at random.
caste, age, sexual orientation, gender identity, marital
status, their twitter followers and their twitter mentions. For
this classification, we used official sources such as ethnic
diversity studies and information in the public domain
such as public profiles, articles written by or about these
                                                                                    HANDLING TWEET LANGUAGES
women, official websites including party websites, Parliament                       Considering India’s linguistic diversity, this study also looked
websites, nomination papers, Lok Dhaba and Wikipedia                                at tweets in languages other than English. Based on an
pages. It is important to note that this is an approximate                          initial sample, Bengali, English, Gujarati, Hindi, Kannada,
classification for the purpose of this analysis, utilising                          Malayalam, Marathi, Tamil and Telugu were found be the
research available in public domain and is not necessarily a                        most commonly used languages in the tweets mentioning
reflection of how each politician identifies herself.                               the women politicians. Keeping this in mind, at the time of
                                                                                    registration, decoders were given the option of choosing one
                                                                                    or more languages from these nine languages for decoding.

SELECTING A TIMELINE                                                                We could not obtain the language classification from Twitter
                                                                                    itself through the third-party service used to access the data.
We aimed to study tweets directed at women politicians                              We instead, detected languages using an automated language
during the three-month period of March-May 2019 - that is,                          detection tool (Google Inc.). Some of the challenges in this
in the lead-up to, during, and shortly after the 2019 General                       approach included, very short content for some tweets, no
Elections of India. Therefore, tweets mentioning women                              access to any user data to estimate language, the ability
politicians over these 3 months, were sampled. The General                          of users to use both Latin and native scripts on Twitter,
Elections ran from 11 April 2019 to 19 May 2019, the                                the usage of ‘Hinglish’ – a mix of Hindi and English – or
results of which were announced on 23 May 2019.                                     other mixtures of languages, etc. Despite its shortcomings,
                                                                                    the classification was useful in engaging Decoders in their
                                                                                    own language and in providing a rough analysis of abuse

OBTAINING AND PROCESSING                                                            in different languages. Importantly, using the available
                                                                                    tools, the Decoders could provide feedback on language

TWITTER DATA                                                                        mischaracterisation.16

OBTAINING SAMPLE TWEETS                                                             DECODING - ANALYSIS OF ABUSE
The women in this study were mentioned in over 7 million
                                                                                    BY VOLUNTEERS
tweets between March and May 2019. Crimson Hexagon (now                             The tweets obtained through random sampling were analysed
part of Brandwatch) was used to obtain a subsample of these                         through a process called ‘Decoding’. Amnesty Decoders
tweets. A random sample of 10,000 tweets were extracted                             is an innovative platform for volunteers around the world
per day as per the Twitter API (Application Program Interface)                      to help Amnesty researchers in analysing large volumes of
terms. The sample included tweets mentioning at least one of                        pictures, documents and social media messages through
the women politicians’ twitter handles and excluded retweets                        their computers and phones. These Decoders were trained in
and tweets that were deleted before the date of extraction.                         identifying and categorising problematic and abusive content.
                                                                                    After a video tutorial, Decoders were asked to select their
Although the API allows 10,000 tweets per day, the actual
                                                                                    language(s) of preference. After being shown a tweet from an
number was lower due to exclusion of deleted or private
                                                                                    obscured username, which mentioned one of the women in
tweets and retweets. We also removed all completely content-
                                                                                    our study, they were asked multiple-choice questions:
free tweets (those that were just a list of @mentions and no
other content) – such tweets amounted to 1.7% of our initial                        1.   “Does the tweet contain problematic or abusive
sample.                                                                                  content?” (No, Problematic, Abusive).

Out of the data obtained from Crimson Hexagon, we sampled                           2.   Unless their answer was No, the follow-up question
TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA 11

       was “What type of problematic or abusive content does                                      not expect perfect alignment between individuals. We also
       it contain?” The Decoders could select one or more                                         compared the agreement amongst the crowd and experts. We
       responses from a list, namely - sexism or misogyny,                                        expected “expert” Amnesty International India staff familiar
       ethnic or religious slur, racism, casteism, homophobia                                     with the issues to have higher agreement than the “crowd” of
       or transphobia, physical threat or sexual threat or a                                      Decoders.
       miscellaneous ‘other’ category for those tweets that
                                                                                                  Agreement amongst raters of tweets was quantified using
       could not be easily categorised.
                                                                                                  two measures: Fleiss’ kappa and intra-class correlation
3.     The Decoders had to further categorise the medium of                                       coefficient.19
       abuse - whether in the form of text, image or a video.
                                                                                                  There was fair agreement of experts on whether tweets
The Decoders could, at any point in time, access the                                              contained problematic, abusive or neutral content and
definitions and examples of problematic and abusive                                               good agreement when the ordinal nature of the classes was
content.17                                                                                        considered (that is, 'Problematic' and “Abusive' answers were
                                                                                                  closer together than 'Abusive' and 'No').

                                                                                                  When grouping together the 'problematic' and 'abusive'
EXPERT ANALYSES                                                                                   classes, expert agreement was moderate. Expert agreement
                                                                                                  with respect to the type of 'problematic' or 'abusive' content
Apart from engaging the Amnesty Decoders, we also had                                             was also fair. As was expected, agreement of Decoders was
796 tweets assessed in-house by Amnesty International                                             lower than that of experts.
India’s staff familiar with the parameters of ‘problematic’ and
                                                                                                  Agreement amongst Decoders was fair when the ordinal
‘abusive’ content. Each tweet was analysed by three experts,
                                                                                                  nature of the classification was taken into consideration.
thus enabling the validation of response. For each of the nine
                                                                                                  Decoder agreement was also fair when grouping together the
languages, three different experts looked at all of the tweets
                                                                                                  'problematic' and 'abusive' classes and also when evaluating
in that particular language, in order to allow for an agreement
                                                                                                  the type of content for tweets classified as such. The analysis
analysis between the experts. People fluent in a particular
                                                                                                  showed that our crowd results were better than random
language decoded tweets of that language. For example, 261
                                                                                                  chance.20
tweets in English were decoded by three experts fluent in
English, while 38 tweets in Marathi were decoded by another
three experts fluent in Marathi.18
                                                                                                  AGGREGATED PROPORTIONS
GENERATED ESTIMATES OF                                                                            ACROSS TWEETS
PROBLEMATIC AND ABUSIVE                                                                           For the descriptive statistics given in the findings of this
                                                                                                  study, we used the same methodology developed by Amnesty

CONTENT                                                                                           International and Element AI for the 2018 Troll Patrol
                                                                                                  Study21, aggregating Decoders’ annotations (“votes”) across
                                                                                                  all tweets in the set, as opposed to aggregating the majority
                                                                                                  vote.22

AGREEMENT ANALYSIS                                                                                Our method of treating each vote as a measure meant that
                                                                                                  the following two scenarios on 10 tweets would lead to the
The Amnesty team investigated whether Decoders generally                                          same aggregated estimate of 30% abuse (simplified as “3
agreed on the classification of a tweet as problematic or                                         abusive tweets”):
abusive. Due to the subjective nature of the analysis, we did

15.   See, Annexure 1, Enrichment
16.   For more information about handling languages, see, Annexure 2
17.   For definitions and examples of problematic and abusive tweets, see, chapter on Methodology, page 14-15
18.   For results of the expert decoding agreement analysis, see, Annexure 5.
19.   (Fleiss, 1971) and intra-class correlation coefficient (Shrout, 1979). https://psycnet.apa.org/buy/1972-05083-001
20.   See, full agreement analysis and methodology, including definitions of fair and moderate agreement in Annexure 5.
21.   Amnesty International, Troll Patrol Findings, https://decoders.amnesty.org/projects/troll-patrol/findings
22.   Laure Delisle et al, Troll Patrol Methodology Note, https://decoders.azureedge.net/data-viz/images/Troll%20Patrol%20-%20Methodology.pdf
12 TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA

•     10 tweets, each labelled as abusive by 30% of its raters,                     the research project many times over, including resource
      and as neutral by the remaining 70% of its raters.                            intensive steps of data collection and crowdsourced labelling,
                                                                                    and comparing the results. However, given the actual
•     10 tweets, 3 of which were labelled as abusive by 100%
                                                                                    unfeasibility of this approach, we instead obtained measures
      of their raters, and the other 7 are labelled as neutral by
                                                                                    of accuracy of the results through the statistical method of
      100% of their raters.
                                                                                    bootstrapping.
This was justified by the agreement analysis discussed
                                                                                    Bootstrapping involved taking 100 random resamples with
in the previous section and Annexure 5. Unlike a visual
                                                                                    replacement from the weighted set of 114,716 labelled
classification task with a clear and objective ground truth,
                                                                                    tweets. Each random resample taken was of the same size as
raters were often divided, especially when the abuse in a
                                                                                    the set of labelled tweets and resampling with replacement
tweet was subtle or contextual. The aggregation of proportions
                                                                                    implied that a specific tweet may have ended up multiple
accounts for this variability of opinions and reflected the most
                                                                                    times in a single resample whereas another tweet may not
accurate picture, without resorting to the extreme solution of
                                                                                    have ended up in the same resample at all.
a majority vote, which dismissed the minority’s perception
of abuse, or considered a tweet abusive if at least one of                          The frequencies of tweets with problematic or abusive
its raters voted as such, which overestimated the overall                           content as well as the frequencies of types of problematic
perception of abuse.                                                                or abusive content presented in this report were calculated
                                                                                    as the medians across the 100 resamples. The uncertainty
                                                                                    associated with these frequencies and conversely thus the

WEIGHTING
                                                                                    robustness of our findings were provided through 95%
                                                                                    confidence intervals, which were calculated as the range
                                                                                    between the 2.5% and 97.5% quantiles across the 100
The answers of the Decoders were re-weighted to account
                                                                                    resamples. The 95% confidence intervals implied, roughly
for sample mismatch with the pool of tweets (the true
                                                                                    speaking, a 95% level of confidence that the intervals
distribution in the “world set”). Weighting factors used for
                                                                                    contained the true frequencies over all of the more than 7
both responses and tweets included: 1) Response weighting
                                                                                    million relevant tweets.
accounting for tweets that were analysed less or more than
the expected number of times (e.g. if one tweet had two                             In short, bootstrapping entailed getting multiple estimates
out of six annotations as Abusive, and one had one out                              of the frequencies, each on a random resample with
of three annotations as Abusive, those contributed in the                           replacement from the actual set of labelled tweets, and
same way); 2) Weighting by tweet collection ‘batch’ used a                          aggregating over the resamples to gauge the accuracy of
uniform sampling per day (e.g. if one date had 20,000 tweets                        our findings. The rationale for employing the bootstrapping
mentioning the women and a second date had 10,000,                                  was that repeated sampling from the empirical distribution
the first date should have had twice as many tweets in the                          of the labelled tweets was the closest approximation to the
sample) and 3) Weighting by tweet language distribution                             aforementioned ideal but unfeasible repeated sampling from
accounting for a difference of completion between different                         the real-world distribution of the over 7 million tweets by
languages for the last batch of data.23                                             conducting the research project.

ACCOUNTED FOR SAMPLING
UNCERTAINTY: BOOTSTRAPPING                                                          LIMITATIONS OF THE STUDY
Every statistical analysis based on a sample must evaluate                          Crude method used for Language detection: Due to limited
the robustness of its findings against the randomness                               resources, we used a very basic method for language
induced by its sampling mechanism. In this study, one major                         detection, namely Google’s language auto detection. This
but controllable source of uncertainty stemmed from the                             method resulted in the language of some tweets being
limitation in data collection: Maximum 10,000 tweets per                            wrongly identified, which could have reduced users'
day, sampled by the provider uniformly at random, among all                         experience as they may have received tweets in a language
the tweets of that day mentioning the politicians identified.                       they had not selected.
This is but a small portion of the much larger volume of such                       English as the preferred language for Decoding: A high
tweets from that day.                                                               proportion of the Decoders selected English as their primary
In an ideal world with unlimited resources, we would have                           language, possibly limiting the diversity of the study.
measured the robustness of our findings by carrying out                             This could be a result of the recruitment efforts that were
                                                                                    primarily in English and the limitations in our outreach as
TROLL PATROL INDIA – EXPOSING ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA 13

an organisation. This also resulted in English tweets being       have influenced our findings.
exhausted quickly, and lesser engagement on the tweets in
                                                                  Timeline: It is important to note that the findings of this study
other languages.
                                                                  are restricted to the time period during which the tweets were
Exclusion of first-time politicians: Women politicians who        sampled i.e. between 1 March 2019 to 31 May 2019. While
contested elections for the first time were excluded in this      the elections were held from 11 April 2019 to 23 May 2019,
study, since at the time of creating the sample of politicians    the period between March and May witnessed extensive
for study, the information on their candidature or nomination     election campaigning. A longer period of tweet samples may
was either not final or available online. Some of these women     have enabled the study to capture the abuse experienced by
experienced online abuse during their campaign and after          women politicians over a longer period of time. It would have
elections, and the study would have benefitted from their         also shed light on the patterns of abuse during normal and
inclusion.                                                        peak times, depending on political events and campaigning
                                                                  around the year.
Tweet sampling and de-identifying: Decoders analysed
random tweets with Twitter handles blurred. We chose to
de-identify the tweets to protect individuals sending and
receiving problematic and abusive content. This meant that
Decoders could not tell if a woman politician was being
targeted herself or merely included as one of a list of names.
The tweets were also shown without the corresponding thread
or conversation due to limited access to Twitter data. This
meant that Decoders could not use the rest of a conversation
to judge a tweet.

Perception of Decoders: The study has relied on the
"perception of the Decoder", which is influenced by their
experiences and prejudices. What may be considered as
problematic or abusive to one may not be considered the
same by another. The project addressed this by showing the
same tweet to many Decoders, which facilitated "validation"
of responses, but it is still possible that “atypical” opinions

23.   See, Annexure 4, Weighting
thatThe
messagesmessages @twitterhandles
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                    notabuse and notwho
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                                                                           abuse. who abuse.
        14 TROLL PATROL INDIA – EXPOSING THE ONLINE ABUSE FACED BY WOMEN POLITICIANS IN INDIA
                                                   *****         *****                       *****
          PROBLEMATIC
PROBLEMATIC
                     PROBLEMATIC

        DECODING TWEETS - DEFINITIONS
             @kavita_krishnan
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          ABUSIVE TWEETS
ABUSIVE TWEETS                                                       *****
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                    WARNING: The examples below contain‘Problematic’-           Problematic tweets contain hurtful or hostile content,
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                    ABUSIVE TWEETS
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                  your disgustingly dirty                   face.***** Maybe
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SEXIST ABUSES
PROBLEMATIC  SEXIST ABUSES
             PROBLEMATIC
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                   @VanzaraDg       फिरमेंपुलग
                                   @NewsHtn रानेजाइये
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                                                  @NewsHtn
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                                                               @seemi_pasha         I have     never seen a  more  ugly woman
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                     SEXIST ABUSES
                   Muslim ass.                                                                                     ‘Abusive’ – Abusive content violates Twitter’s own rules and includes tweets
                              name फिर पुराने काम में लग जाइये
                     ABUSIVE TWEETS
                     @blurred
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                                                                    गई तू promote
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                                                                                       में ऊपर आयी है ये or                                    threaten people based on their race,
                     सबको पता है                                    ethnicity, national origin, sexual                                         orientation, gender, gender identity, religious
                                                                                                           affiliation, age, disability, or serious disease.
                                                   *****         *****

                                                                                                           Examples include: physical or sexual threats, wishes for the physical harm or
           SEXIST ABUSES
SEXIST ABUSES
                                                                                                           death, reference to violent events, behaviour that incites fear or repeated slurs,
              फिर पुname
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                                                                               राजनीफत
                                                                                   आयीमेंहैऊपर
                                                                                            ये आयी है ये   epithets, racist and sexist tropes, or other content that degrades someone.
सबको पता हैसबको पता है
                                                                                             *****

                     SEXIST ABUSES

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                                                                                                                                                                                                                                                           norwho
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                               *****   *****
              @kavita_krishnan @VanzaraDg    THAT
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                                                   @seemi_pasha VIOLENCE
                                                                 @kavita_krishnan
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                                                                                  *****              WOMEN.
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          PROBLEMATIC
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                                                                                                                                     caused u to eat some smelly Muslim ass.                                                                                                                                                                                     r

ABUSIVE TWEETS ABUSIVE
          ABUSIVE TWEETS                TWEETS                                                                        ABUSIVE TWEETS
                                                                                                            ABUSIVE TWEETS
                                                                                                                                       ABUSIVE TWEETS                                                                             ABUSIVEABUSIVE
                                                                                                                                                                                                                        ABUSIVE TWEETS   TWEETS                    TWEETS

                        Note
                        @      for All:xxThe
                           ssssxxxxx      Are Tweet
                                              gaddariexamples
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                                                                                                                                                                                          https://t.co/DTJYlhohvY
                                                                                                                                                                                        national,
                        too graphic, let me know. Will use 1-2 examples each.                                                        Madarsa jehadi aunty. It’s looking clear cut                                                                either(note:
                                                                                                                                                                                                                                                        in jailvideo hasher
                                                                                                                                                                                                                                                                or send  been   removed These
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                                                                                                                                    too graphic, let me know. Will use 1-2 examples each.                                                       too  graphic,    let me   know.    Will use  1-2 examples each.
                                                                                                                                     photoshoped and you are making issue of                                                                      shameless people are harming the integrity of
                                                                                                                                     this.. Use your common sense PattharBaaz                                                                     the nation.
                                                          *****           *****                                                      aurat                *****     *****                                                                                                         *****           *****
                    WARNING: The examples below contain explicit                                              WARNING:   and threatening
                                                                                                                                     The examples below contain explicit                                 WARNING:  and threatening
                                                                                                                                                                                                                                The examples below contain explic
SEXIST ABUSES SEXIST ABUSES
                    messages
              NoteTweet
                   for All: The Tweet     that     show       violence          against          women.SEXIST  ABUSES                                                             SEXISTforABUSESSEXIST   ABUSES
Note for All: The       examples  willexamples
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                ones. If any of you feel that any one of the following abuses should not be considered
ones. If any of@blurred
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                                                                       तू कैसे        तू कमें
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सबको पता हैसबको natureपता है of abuse and not the persons who are abused,                                          nor   the    persons           who    abuse.
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                                                                                                        सबको पता है      सबको       पता   है
                                                                                                                                   woman than you. A wild pig would be prettier                                            सबको       पता  है
                                                                                                                                                                                                                                            सबको      पता     है
                    Insulting
                      SEXIST
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                                                                                                                       थर्ड
                                                                                                                @blurred    क्लास
                                                                                                                             name  औरत
                                                                                                                                     थर्ड क्लास
                                                                                                                                   who    तूabuse.
                                                                                                                                             अपना
                                                                                                                                              @ औरत पास्टतू भूअपना
                                                                                                                                                               ल गईपास्ट
                                                                                                                                                   MamataOfficial   तूPut
                                                                                                                                                                        कैthis
                                                                                                                                                                          से
                                                                                                                                                                          भूलराजनीफत
                                                                                                                                                                               गई तू कमें
                                                                                                                                                                                      ै सेऊपर
                                                                                                                                                                                           राजनीफत
                                                                                                                                                                                               आयी मेंहैऊपर
                                                                                                                                                                                                 @blurredये आयी
                                                                                                                                                                                                            nameहै थर्ड
                                                                                                                                                                                                                   ये क्लास
                                                                                                                                                                                                             @blurred      name  औरत
                                                                                                                                                                                                                                   थर्ड क्लास
                                                                                                                                                                                                                                          तू अपना औरत पास्टतू भूअपना
                                                                                                                                                                                                                                                                 ल गईपास्ट
                                                                                                                                                                                                                                                                         तू कैसे
                                                                                                                                                                                                                                                                              भूलराजनीफत
                                                                                                                                                                                                                                                                                    गई तू कमें
                                                                                                                                                                                                                                                                                           ै सेऊपर
                                                                                                                                                                                                                                                                                                राजनीफत
                                                                                                                                                                                                                                                                                                    आयीमेंहैऊपर
                                                                                                                                                                                                                                                                                                             ये आयी है ये
सबको पता हैसबको पता है                                                                          सबको पता हैसबको पता           है idiot anti national, dangerous lady
                                                                                                                        stupid,                                                                  सबको पता हैसबको पता है
WARNING:           Discriminatory,
                    SEXIST ABUSES
             WARNING:
                The     examples
                               The    examples
                                       below      offensive
                                                 contain
                                                    below       explicitor
                                                                  contain   andexplicit
                                                                                   threatening
                                                                                          and    threatening Direct         or
                                                                                                                       SEXIST    indirect
                                                                                                                                   ABUSES         threats        of
                                                                                                                        either in jail or send her to pakistan. Thesephysical                                Direct SEXIST or  indirect            threats
                                                                                                                                                                                                                                 ABUSES @priyankagandhi               of    sexual
messagesmessages
               that showthat    violence
                                    show against
                                              violencewomen. against women.                                             shameless people are harming the integrity of                                                @@ @TOIIndiaNews
                                                                                                                                                                                                                           TOIIndiaNews
                                                                                                                                                                                                                           TOIIndiaNews@@          priyankagandhi
                                                                                                                                                                                                                                                    priyankagandhiuuudon’t    don’t
                                                                                                                                                                                                                                                                              don’t
                 insulting content directed at                                                                      violence
                                                                                                                        the nation. or wishes for serious                                                   violence have
                                                                                                                                                                                                                        have  or
                                                                                                                                                                                                                        havebrain
                                                                                                                                                                                                                              brain
                                                                                                                                                                                                                              brainwishes
                                                                                                                                                                                                                                     toto
                                                                                                                                                                                                                                        todecide
                                                                                                                                                                                                                                            decide
                                                                                                                                                                                                                                            decidefromfor
                                                                                                                                                                                                                                                      from    rape
                                                                                                                                                                                                                                                       fromwher
                                                                                                                                                                                                                                                              wher        or other
                                                                                                                                                                                                                                                               wheruuushould
                                                                                                                                                                                                                                                                         should
                                                                                                                                                                                                                                                                         should  fight
                                                                                                                                                                                                                                                                                  fight
                                                                                                                                                                                                                                                                                   fight
The @twitterhandles
             The @twitterhandles
                      and @mentions
                    @blurred        and have
                                       name      फिर
                                          @mentions
                                              been blurred
                                                      have पुरbeen
                                                                ाने blurred
                                                              since  काम
                                                                     the     में लग
                                                                         purpose
                                                                             sincehere जाइये
                                                                                   the purpose*****
                                                                                       is to highlight
                                                                                                here isthe             @blurred name फिर पुराने काम में लग जाइये
                                                                                                          to highlight the                                                                                          @blurred
                                                                                                                                                                                                                    @     TOIIndiaNews    name  @female,फिर      पु
                                                                                                                                                                                                                                                     priyankagandhi र ाने   काम
                                                                                                                                                                                                                                                                              uso    में लग जाइये
                                                                                                                                                                                                                                                                               sodon’t
nature of abuse  a ofnot
             nature
                and woman
                        abuse       based
                          the persons
                               and not who       onwho
                                        the persons
                                            are abused,hernor    caste,
                                                            arethe
                                                                 abused,
                                                                   personsnorwho
                                                                              the persons
                                                                                  abuse. who abuse.             physical harm, death, or disease.                                                                       forms
                                                                                                                                                                                                                     election?
                                                                                                                                                                                                                        election?
                                                                                                                                                                                                                        election?   of
                                                                                                                                                                                                                                  Brainless sexual
                                                                                                                                                                                                                                    Brainless
                                                                                                                                                                                                                                    Brainless       female,assault.
                                                                                                                                                                                                                                                     female,    she
                                                                                                                                                                                                                                                                  she
                                                                                                                                                                                                                                                                  shelooks
                                                                                                                                                                                                                                                                       looks
                                                                                                                                                                                                                                                                        looksso
                    @blurred
                    PROBLEMATIC        name      थर्ड   क्लास        औरत       तू अपना      पास्ट      भू ल   गई    तू  क ै से
                                                                                                                       @blurred राजनीफत   nameमें  ऊपर
                                                                                                                                                     थर्ड   आयी
                                                                                                                                                              क्लास है औरतये    तू अपना       पास्ट    भू ल गई  तू  क
                                                                                                                                                                                                                    have brain to decide from wher u should तू
                                                                                                                                                                                                                      ै  से
                                                                                                                                                                                                                     horribleराजनीफत
                                                                                                                                                                                                                        horrible
                                                                                                                                                                                                                        horrible
                                                                                                                                                                                                                    @blurred     she
                                                                                                                                                                                                                                  she
                                                                                                                                                                                                                                  she   thinksमें
                                                                                                                                                                                                                                          thinks
                                                                                                                                                                                                                                           thinks
                                                                                                                                                                                                                                          name    ऊपर   थर्ड
                                                                                                                                                                                                                                                  herself
                                                                                                                                                                                                                                                   herself
                                                                                                                                                                                                                                                   herself     आयी
                                                                                                                                                                                                                                                               क्लास
                                                                                                                                                                                                                                                              Indira
                                                                                                                                                                                                                                                               Indira
                                                                                                                                                                                                                                                                Indira   है औरत
                                                                                                                                                                                                                                                                             ये
                                                                                                                                                                                                                                                                       Gandhi’s
                                                                                                                                                                                                                                                                         Gandhi’s
                                                                                                                                                                                                                                                                         Gandhi’s        अपना पास्ट भूल गई तू कैस
                                                                                                                                                                                                                                                                                      fight
                including
                    सबको पताcontent    है          that aims to                                                        सबको पता है                                                                                  सबको
                                                                                                                                                                                                                     replica
                                                                                                                                                                                                                        replica
                                                                                                                                                                                                                        replicamy
                                                                                                                                                                                                                    election?    पता
                                                                                                                                                                                                                                 my
                                                                                                                                                                                                                                 my  foot.है She
                                                                                                                                                                                                                                       foot.
                                                                                                                                                                                                                                        foot.
                                                                                                                                                                                                                                    Brainless  She
                                                                                                                                                                                                                                               Shelooks
                                                                                                                                                                                                                                                      looks
                                                                                                                                                                                                                                                       lookslike
                                                                                                                                                                                                                                                     female,   like
                                                                                                                                                                                                                                                                likeshe
                                                                                                                                                                                                                                                                    transgender
                                                                                                                                                                                                                                                                     transgender
                                                                                                                                                                                                                                                                      transgender
                                                                                                                                                                                                                                                                          looks so
                   @kavita_krishnan
               attack,  harm,@belittle,   @VanzaraDg
                                            *****         @NewsHtn
                                                      *****
                                                 humiliate     or     @seemi_pasha I have never seen a more ugly woman                                                                                                                           horrible she thinks herself Indira Gandhi’s
                  @ Arvindvijay_
               than you.            LambaAlka
                               A wild   pig wouldChunaavkeBaad
                                                     be prettier than your disgustingly dirty face. Maybe constant rejection
PROBLEMATIC
          PROBLEMATIC                                                                                                                                                                                                                            replica my foot. She looks like transgender
               undermine       her or her    hascommunity.
                  Laura dunga https://t.co/DTJYlhohvY
                   from ur community              caused u to eat some smelly Muslim ass.
@kavita_krishnan
             @kavita_krishnan
                   @VanzaraDg
                   (note: video @VanzaraDg
                                 @NewsHtn    @seemi_pasha
                                               @NewsHtn
                                 has been removed       from@seemi_pasha
                                                              I have never seen
                                                             platform)      I havea more
                                                                                    never ugly
                                                                                          seenwoman
                                                                                                a more ugly woman
than you. A wild
             thanpig
                   you.
                     would
                        A wild
                           be pig
                               prettier
                                  would than
                                          be prettier
                                             your disgustingly
                                                      than yourdirty
                                                                 disgustingly
                                                                     face. Maybe
                                                                              dirtyconstant
                                                                                    face. Maybe
                                                                                             rejection
                                                                                                 constant rejection
from ur community
             from urhas
                      community
                        caused u has
                                  to eat
                                      caused
                                          someusmelly
                                                to eat Muslim
                                                        some smelly
                                                                ass. Muslim ass.           *****
                                                          *****           *****                                               @@@EconomicTimes
                                                                                                                                   EconomicTimes
                                                                                                                                   EconomicTimes@@        @MamataOfficial
                                                                                                                                                            MamataOfficial
                                                                                                                                                             MamataOfficialPut   Put
                                                                                                                                                                                  Putthis
                                                                                                                                                                                      this
                                                                                                                                                                                       this
                                                                                                                              stupid,
                                                                                                                               stupid,
                                                                                                                                stupid,idiot
                                                                                                                                          idiot
                                                                                                                                           idiotanti
                                                                                                                                                  anti
                                                                                                                                                  antinational,
                                                                                                                                                       national,
                                                                                                                                                        national,dangerous
                                                                                                                                                                    dangerous
                                                                                                                                                                    dangerouslady lady
                                                                                                                                                                                   lady
                                                                                                                              either
                                                                                                                               either
                                                                                                                                eitherinin
                                                                                                                                         injail
                                                                                                                                            jail
                                                                                                                                             jailoror
                                                                                                                                                   orsend
                                                                                                                                                      send
                                                                                                                                                      sendher
                                                                                                                                                           her
                                                                                                                                                            hertoto
                                                                                                                                                                  topakistan.
                                                                                                                                                                     pakistan.
                                                                                                                                                                     pakistan.These
                                                                                                                                                                                 These
                                                                                                                                                                                 These
ABUSIVE TWEETS ABUSIVE
          ABUSIVE TWEETS                TWEETS                                                                                shameless
                                                                                                                               shameless
                                                                                                                                shamelesspeople
                                                                                                                      @ EconomicTimes          people
                                                                                                                                                people
                                                                                                                                                @       are
                                                                                                                                                         are
                                                                                                                                                         areharming
                                                                                                                                                             harming
                                                                                                                                                              harmingthe
                                                                                                                                                     MamataOfficial      the
                                                                                                                                                                          the
                                                                                                                                                                          Putintegrity
                                                                                                                                                                              integrity
                                                                                                                                                                               integrity
                                                                                                                                                                                this ofof of
                                                                                                                              the
                                                                                                                               the
                                                                                                                                thenation.
                                                                                                                                    nation.
                                                                                                                                    nation.
                                                                                                                      stupid, idiot anti national, dangerous lady
                                                                                                                      either in jail or send her to pakistan. These
                                                                                                                      shameless people are harming the integrity of
                                                                                                                      the nation.
                      @ TOIIndiaNews @ priyankagandhi u don’t
                      have brain to decide from wher u should fight
                      election? Brainless female, she looks so
                      horrible she thinks herself Indira Gandhi’s
                      replica my foot. She looks like transgender
                                                          *****           *****

SEXIST ABUSES
           SEXIST ABUSES
                                                                                                          *****
@blurred name
@blurred name
              फिर पुname
          @blurred
              थर्ड क्लास
          @blurred  name
                           HOMOPHOBIA OR
                     राने काम
                           फिरमेंपुलग
                          औरत
                                   रानेजाइये
                           थर्ड क्लास
                                तू अपना
                                       काम में लग जाइये
                                       औरत
                                         पास्टतू भूअपना
                                                    ल गईपास्ट
                                                         तू कैसे
                                                              भूलराजनीफत
                                                                   गई तू कमें
                                                                          ै सेऊपर
                                                                               राजनीफत
                                                                                   आयीमेंहैऊपर
                                                                                            ये आयी है ये
                                                                                                                                                        OTHER
सबको पता हैसबको पता है      TRANSPHOBIA                           There will be some tweets that fall
                  SEXIST ABUSES
            Discriminatory, offensive or insulting                under the ‘other category’ that are
                  @blurred
             content   directed at फिर
                             name      पुराने काम
                                   a woman        में लग जाइये
                                               based                problematic and/or abusive. For
                  @blurred name थर्ड क्लास औरत तू अपना पास्ट भूल गई तू कैसे राजनीफत में ऊपर आयी है ये
              on her sexual orientation, gender                   example, statements that target a
                  सबको पता है
                identity or gender expression.                     user’s disability, be it physical or
              This includes negative comments                      mental, or content that attacks a
             towards bisexual, homosexual and                     woman’s nationality, health status,
                      transgender people.                            legal status, employment, etc.
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