Annotating Online Misogyny - Leon Derczynski
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Annotating Online Misogyny
Philine Zeinert Nanna Inie Leon Derczynski
IT University of Copenhagen IT University of Copenhagen IT University of Copenhagen
Denmark Denmark Denmark
phze@itu.dk nans@itu.dk leod@itu.dk
Abstract Abusive language is linguistically diverse (Vid-
gen and Derczynski, 2020), both explicitly, in the
Online misogyny, a category of online abusive form of swear words or profanities; implicitly, in
language, has serious and harmful social con- the form of sarcasm or humor (Waseem et al.,
sequences. Automatic detection of misogynis- 2017); and subtly, in the form of attitudes and opin-
tic language online, while imperative, poses
ions. Recognizing distinctions between variants of
complicated challenges to both data gathering,
data annotation, and bias mitigation, as this misogyny is challenging for humans, let alone com-
type of data is linguistically complex and di- puters. Systems for automatic detection are usually
verse. This paper makes three contributions created using labeled training data (Kiritchenko
in this area: Firstly, we describe the detailed et al., 2020), hence, their performance depends
design of our iterative annotation process and on the quality and representativity of the available
codebook. Secondly, we present a comprehen- datasets and their labels. We currently lack trans-
sive taxonomy of labels for annotating misog-
parent methods for how to create diverse datasets.
yny in natural written language, and finally, we
introduce a high-quality dataset of annotated
When abusive language is annotated, classes are of-
posts sampled from social media posts. ten created based on each unique dataset (a purely
inductive approach), rather than taking advantage
1 Introduction of general, established terminology from, for in-
stance, social science or psychology (a deductive
Abusive language is a phenomenon with serious approach, building on existing research). This
consequences for its victims, and misogyny is makes classification scores difficult to compare and
no exception. According to a 2017 report from apply across diverse training datasets.
Amnesty International, 23% of women from eight This paper investigates the research question:
different countries have experienced online abuse How might we design a comprehensive annotation
or harassment at least once, and 41% of these said process which results in high quality data for au-
that on at least one occasion, these online experi- tomatically detecting misogyny? We make three
ences made them feel that their physical safety was novel contributions: 1. Methodology: We describe
threatened (Amnesty International, 2017). our iterative approach to the annotation process in
Automatic detection of abusive language can a transparent way which allows for a higher degree
help identify and report harmful accounts and acts, of comparability with similar research. 2. Model:
and allows counter narratives (Chung et al., 2019; We present a taxonomy and annotation codebook
Garland et al., 2020; Ziems et al., 2020). Due to grounded in previous research on automatic detec-
the volume of online text and the mental impact tion of misogyny as well as social science termi-
on humans who are employed to moderate online nology. 3. Dataset: We present a new, annotated
abusive language - moderators of abusive online corpus of Danish social media posts, Bajer,1 an-
content have been shown to develop serious PTSD notated for misogyny, including analysis of class
and depressive symptoms (Casey Newton, 2020) - balance, word frequencies, Inter-Annotator Agree-
it is urgent to develop systems to automate the de- ment (IAA), annotation errors, and classification
tection and moderation of online abusive language. baseline.
Automatic detection, however, presents significant 1
https://github.com/phze22/
challenges (Vidgen et al., 2019). Online-Misogyny-in-Danish-BajerSince research has indicated that misogyny from the work of Waseem and Hovy (2016). While
presents differently across languages, and, likely, harsh sexism (hateful or negative views of women)
cultures (Anzovino et al., 2018), an additional con- is the more recognized type of sexism, benevo-
tribution of this work is that it presents a dataset lent sexism (“a subjectively positive view towards
of misogyny in Danish, a North Germanic lan- men or women”), often exemplified as a compli-
guage, spoken by only six million people, and ment using a positive stereotypical picture, is still
indeed the first work of its kind in any Scandina- discriminating (Glick and Fiske, 1996). Other cat-
vian/Nordic culture to our knowledge. In Denmark egorisations of harassment towards women have
an increasing proportion of people refrain from on- distinguished between physical, sexual and indirect
line discourse due to the harsh tone, with 68% of occurrences (Sharifirad and Jacovi, 2019).
social media users self-excluding in 2021 (Anal- Anzovino et al. (2018) classify misogyny more
yse & Tal, 2021; Andersen and Langberg, 2021), segregated in five subcategories: Discredit, Harass-
making this study contextually relevant. Further, ment & Threats of Violence, Derailing, Stereotype
the lack of language resources available for Dan- & Objectification, and Dominance. They also dis-
ish (Kirkedal et al., 2019) coupled with its lexical tinguish between if the abuse is active or passive
complexity (Bleses et al., 2008) make it an intricate towards the target. These labels appear to apply
research objective for natural language processing. well to other languages, and quantitative represen-
tation of labels differ by language. For example,
2 Background and related work Spanish shows a stronger presence of Dominance,
Abusive language is as ancient a phenomenon as Italian of Stereotype & Objectification, and English
written language itself. Written profanities and in- of Discredit. As we see variance across languages,
sults about others are found as old as graffiti on ru- building terminology for labeling misogyny cor-
ins from the Roman empire (Wallace, 2005). Auto- rectly is therefore a key challenge in being able to
matic processing of abusive text is far more recent, detect it automatically. Parikh et al. (2019) take
early work including e.g. Davidson et al. (2017) a multi-label approach to categorizing posts from
and Waseem et al. (2017). Research in this field the “Everyday Sexism Project”, where as many
has produced both data, taxonomies, and methods as 23 different categories are not mutually exclu-
for detecting and defining abuse, but there exists no sive. The types of sexism identified in their dataset
objective framing for what constitutes abuse and include body shaming, gaslighting, and mansplain-
what does not. In this work, we focus on a specific ing. While the categories of this work are extremely
category of online abuse, namely misogyny. detailed and socially useful, several studies have
demonstrated the challenge for human annotators
2.1 Online misogyny and existing datasets to use labels that are intuitively unclear (Chatzakou
et al., 2017; Vidgen et al., 2019) or closely related
Misogyny can be categorised as a subbranch of hate
to each other (Founta et al., 2018).
speech and is described as hateful content targeting
women (Waseem, 2016). The degree of toxicity Guest et al. (2021) suggest a novel taxonomy for
depends on complicated subjective measures, for misogyny labeling applied to a corpus of primarily
instance, the receiver’s perception of the dialect of English Reddit posts. Based on previous research,
the speaker (Sap et al., 2019). including Anzovino et al. (2018), they present the
Annotating misogyny typically requires more following four overarching categories of misog-
than a binary present/absent label. Chiril et al. yny: (i) Misogynistic Pejoratives, (ii) descriptions
(2020), for instance, use three categories to classify of Misogynistic Treatment, (iii) acts of Misogynis-
misogyny in French: direct sexist content (directly tic Derogation and (iv) Gendered Personal attacks
addressed to a woman or a group of women), de- against women.
scriptive sexist content (describing a woman or The current work combines previous categoriza-
women in general) or reporting sexist content (a tions on misogyny into a taxonomy which is useful
report of a sexism experience or a denunciation of for annotation of misogyny in all languages, while
a sexist behaviour). This categorization does not, being transparent about the construction of this
however, specify the type of misogyny. taxonomy. Our work builds on the previous work
Jha and Mamidi (2017) distinguish between presented in this section, continuous discussions
harsh and benevolvent sexism, building on the data among the annotators, and the addition of socialscience terminology to create a single-label tax- Different social media platforms attract differ-
onomy of misogyny as identified in Danish social ent user groups and can exhibit domain-specific
media posts across various platforms. language (Karan and Šnajder, 2018). Rather than
choosing one platform (existing misogyny datasets
3 Methodology and dataset creation are primarily based on Twitter and Reddit (Guest
The creation of quality datasets involves a chain of et al., 2021)), we sampled from multiple platforms:
methodological decisions. In this section, we will Statista (2020) shows that the platform where most
present the rationale of creating our dataset under Danish users are present is Facebook, followed
three headlines: Dataset, Annotation process, and by Twitter, YouTube, Instagram and lastly, Reddit.
Mitigating biases. The dataset was sampled from Twitter, Facebook
and Reddit posts as plain text.
3.1 Dataset: Online misogyny in social media
Language variety: Danish, BCP-47: da-DK.
Bender and Friedman (2018) present a set of data
statements for NLP which help “alleviate issues re- Text characteristics: Danish colloquial web
lated to exclusion and bias in language technology, speech. Posts, comments, retweets: max. length
lead[ing] to better precision in claims about how 512, average length: 161 characters.
natural language processing research can general- Speaker demographics: Social media users,
ize and thus better engineering results”. age/gender/race unknown/mixed.
Data statements are a characterization of a
dataset which provides context to others to under- Speech situation: Interactive, social media dis-
stand how experimental results might generalize cussions.
and what biases might be reflected in systems built
Annotator demographics: We recruited anno-
on the software. We present our data statements for
tators aiming specifically for diversity in gender,
the dataset creation in the following:
age, occupation/ background (linguistic and ethno-
Curation rationale: Random sampling of text graphic knowledge), region (spoken dialects) as
often results in scarcity of examples of specifically well as an additional facilitator with a background
misogynistic content (e.g. (Wulczyn et al., 2017; in ethnography to lead initial discussions (see Table
Founta et al., 2018)). Therefore, we used the com- 1). Annotators were appointed as full-time employ-
mon alternative of collecting data by using pre- ees with full standard benefits.
defined keywords with a potentially high search hit
Gender: 6 female, 2 male (8 total)
(e.g. Waseem and Hovy (2016)), and identifying
Age: 5& Test stages are replaced by Leveraging of an- users (Wiegand et al., 2019), domain (Wiegand
notations for one’s particular goal, in our case the et al., 2019), time (Florio et al., 2020) and lack of
creation of a comprehensive taxonomy. linguistic variety (Vidgen and Derczynski, 2020).
We created a set of guidelines for the annotators.
Label biases: Label biases can be caused by, for
The annotators were first asked to read the guide-
instance, non-representative annotator selection,
lines and individually annotate about 150 different
lack in training/domain expertise, preconceived
posts, after which there was a shared discussion.
notions, or pre-held stereotypes. These biases are
After this pilot round, the volume of samples per an-
treated in relation to abusive language datasets
notator was increased and every sample labeled by
by several sources, e.g. general sampling and
2-3 annotators. When instances were ‘flagged’ or
annotators biases (Waseem, 2016; Al Kuwatly
annotators disagreed on them, they were discussed
et al., 2020), biases towards minority identity
during weekly meetings, and misunderstandings
mentions based for example on gender or race
were resolved together with the external facilita-
(Davidson et al., 2017; Dixon et al., 2018; Park
tor. After round three, when reaching 7k annotated
et al., 2018; Davidson et al., 2019), and political
posts (Figure 2), we continued with independent
annotator biases (Wich et al., 2020). Other quali-
annotations maintaining a 15% instance overlap
tative biases comprise, for instance, demographic
between randomly picked annotator pairs.
bias, over-generalization, topic exposure as social
Management of annotator disagreement is an im-
biases (Hovy and Spruit, 2016).
portant part of the process design. Disagreements
can be solved by majority voting (Davidson et al.,
Systematic measurement of biases in datasets
2017; Wiegand et al., 2019), labeled as abuse if at
remains an open research problem. Friedman and
least one annotator has labeled it (Golbeck et al.,
Nissenbaum (1996) discuss “freedom from biases”
2017) or by a third objective instance (Gao and
as an ideal for good computer systems, and state
Huang, 2017). Most datasets use crowdsourcing
that methods applied during data creation influ-
platforms or a few academic experts for annotation
ence the quality of the resulting dataset quality
(Vidgen and Derczynski, 2020). Inter-annotator-
with which systems are later trained. Shah et al.
agreement (IAA) and classification performance
(2020) showed that half of biases are caused by
are established as two grounded evaluation mea-
the methodology design, and presented a first ap-
surements for annotation quality (Vidgen and Der-
proach of classifying a broad range of predictive
czynski, 2020). Comparing the performance of am-
biases under one umbrella in NLP.
ateur annotators (while providing guidelines) with
We applied several measures to mitigate biases
expert annotators for sexism and racism annotation,
occurring through the annotation design and execu-
Waseem (2016) show that the quality of amateur
tion: First, we selected labels grounded in existing,
annotators is competitive with expert annotations
peer-reviewed research from more than one field.
when several amateurs agree. Facing the trade-off
Second, we aimed for diversity in annotator profiles
between training annotators intensely and the num-
in terms of age, gender, dialect, and background.
ber of involved annotators, we continued with the
Third, we recruited a facilitator with a background
trained annotators and group discussions/ individ-
in ethnographic studies and provided intense anno-
ual revisions for flagged content and disagreements
tator training. Fourth, we engaged in weekly group
(Section 5.4).
discussions, iteratively improving the codebook
3.3 Mitigating Biases and integrating edge cases. Fifth, the selection of
platforms from which we sampled data is based on
Prior work demonstrates that biases in datasets local user representation in Denmark, rather than
can occur through the training and selection of convenience. Sixth, diverse sampling methods for
annotators or selection of posts to annotate (Geva data collection reduced selection biases.
et al., 2019; Wiegand et al., 2019; Sap et al., 2019;
Al Kuwatly et al., 2020; Ousidhoum et al., 2020). 4 A taxonomy and codebook for labeling
online misogyny
Selection biases: Selection biases for abusive
language can be seen in the sampling of text, for in- Good language taxonomies systematically bring
stance when using keyword search (Wiegand et al., together definitions and describe general principles
2019), topic dependency (Ousidhoum et al., 2020), of each definition. The purpose is categorizingreference lang. labels
Abusive Zampieri et al. (2019) da,en, Offensive (OFF)/Not offensive (NOT)
Language gr,ar,tu Targeted Insult (TIN)/Untargeted (UNT)/
Individual (IND)/Group (GRP)/Other (OTH)
Hate speech Waseem and Hovy (2016) en Sexism, Racism
Misogyny Anzovino et al. (2018) en,it,es Discredit, Stereotype, Objectification,
Sexual Harassm., Dominance, Derailing
Jha and Mamidi (2017) en Benevolent extension
Table 2: Established taxonomies and their use for the misogyny detection task
and mapping entities in a way that demonstrates stances of abusive language, our taxonomy embeds
their natural relationship, e.g. Schmidt and Wie- misogyny as a subcategory of abusive language.
gand (2017); Anzovino et al. (2018); Zampieri et al. Misogyny is distinguished from, for instance, per-
(2019); Banko et al. (2020). Their application is sonal attacks, which is closer to the abusive lan-
especially clear in shared tasks, as for multilingual guage of cyberbullying. For definitions and ex-
sexism detection against women, SemEval 2019 amples from the dataset to the categories, see Ap-
(Basile et al., 2019). pendix A.1. We build on the taxonomy suggested
On one hand, it should be an aim of a taxon- in Zampieri et al. (2019), which has been applied to
omy that it is easily understandable and applicable datasets in several languages as well as in SemEval
for annotators from various background and with (Zampieri et al., 2020). While Parikh et al. (2019)
different expertise levels. On the other hand, a provide a rich collection of sexism categories, mul-
taxonomy is only useful if it is also correct and tiple, overlapping labels do not fulfill the purpose of
comprehensive, i.e. a good representation of the being easily understandable and applicable for an-
world. Therefore, we have aimed to integrate defi- notators. The taxonomies in Anzovino et al. (2018)
nitions from several sources of previous research and Jha and Mamidi (2017) have proved their ap-
(deductive approach) as well as categories result- plication to English, Italian and Spanish, and of-
ing from discussions of the concrete data (inductive fer more general labels. Some labels from previ-
approach). ous work were removed from the labeling scheme
Our taxonomy for misogyny is the product of (a) during the weekly discussions among authors and
existing research in online abusive language and annotators, (for instance derailing), because no in-
misogyny (specifically the work in Table 2), (b) a stances of them were found in the data.
review of misogyny in the context of online plat-
forms and online platforms in a Danish context (c) 4.1 Misogyny: Neosexism
iterative adjustments during the process including During our analysis of misogyny in the Danish
discussions between the authors and annotators. context (b), we became aware of the term “neosex-
The labeling scheme (Figure 1) is the main ism”. Neosexism is a concept defined in Tougas
structure for guidelines for the annotators, while a et al. (1999), and presents as the belief that women
codebook ensured common understanding of the have already achieved equality, and that discrimi-
label descriptions. The codebook provided the an- nation of women does not exist. Neosexism is based
notators with definitions from the combined tax- on covert sexist beliefs, which can “go unnoticed,
onomies. The descriptions were adjusted to dis- disappearing into the cultural norms. Those who
tinguish edge-cases during the weekly discussion consider themselves supporters of women’s rights
rounds. may maintain non-traditional gender roles, but also
The taxonomy has four levels: (1) Abu- exhibit subtle sexist beliefs” (Martinez et al., 2010).
sive (abusive/not abusive), (2) Target (indi- Sexism in Denmark appear to correlate with the
vidual/group/others/untargeted), (3) Group type modern sexism scale (Skewes et al., 2019; Tougas
(racism/misogyny/others), (4) Misogyny type et al., 1995; Swim et al., 1995; Campbell et al.,
(harassment/discredit/stereotype & objectifica- 1997). Neosexism was added to the taxonomy be-
tion/dominance/neosexism/benevolent). To demon- fore annotation began, and as we will see in the
strate the relationship of misogyny to other in- analysis section, neosexism was the most common1.3K
neosexism
denial of discrimination/
resentment of complaints
0.3K
discredit
disgrace/ humiliate women
3K
individual 2K
with no larger intent
person-targeted,
e.g. cyberbullying
misogyny 0.2K stereotypes &
towards women
20.4K
not 2.6K objectification
group
normative held but fixed,
0.5K
7.5K
abusive
group-targeted,
i.e. hate speech
others
oversimplified gender images
e.g. LGTB, towards men 0.07K
following 11-point-list
after Waseem & Hovy 1K untargeted
benevolent sexism
positive, gender-typical sentiment,
(2016) often disguised as a compliment
profanity/ swearing 0.1K
racism 0.06K
0.8K others
because of ethnicity dominance
advocating superiority
e.g. conceptual against the
0.05K
media, a political party
sexual harassment
asking for sexual favours,
unwanted sexualisation
Figure 1: A labeling scheme for online misogyny (blue) embedded within the taxonomy for labeling abusive
language occurrences (green). Definitions and examples can be found in the compressed codebook in A.1
form of misogyny present in our dataset (Figure 1). onomies in abusive language while integrating
Here follow some examples of neosexism from our context-related occurrences. A similar idea is
dataset: demonstrated in Mulki and Ghanem (2021), adding
• Resenting complaints about discrimination: damning as an occurrence of misogyny in an Ara-
“I often feel that people have treated me better bic context. While most of previous research is
and spoken nicer to me because I was a girl, done in English, these language-specific findings
so I have a hard time taking it seriously when highlight the need for taxonomies that are flexible
people think that women are so discriminated to different contexts, i.e. they are good represen-
against in the Western world.” tations of the world. Lastly, from an NLP point
• Questioning the existence of discrimination: of view, languages with less resources for training
“Can you point to research showing that child- data can profit further from transfer learning with
birth is the reason why mothers miss out on similar labels, as demonstrated in Pamungkas et al.
promotions?” (2020) for misogyny detection.
• Presenting men as victims: “Classic. If it’s a
disadvantage for women it’s the fault of soci- 5 Results and Analysis
ety. If men, then it must be their own. Sexism 5.1 Class Balance
thrives on the feminist wing.”
The final dataset contains 27.9K comments, of
Neosexism is an implicit form of misogyny, which
which 7.5K contain abusive language. Misogy-
is reflected in annotation challenges summarised
nistic posts comprise 7% of overall posts. Neosex-
in section 5.5. In prior taxonomies, instances of
ism is by far the most frequently represented class
neosexism would most likely have been assigned to
with 1.3K tagged posts, while Discredit and Stereo-
the implicit appearances of misogynistic treatment
type & objectification are present in 0.3K and 0.2K
(ii) (Guest et al., 2021) – or perhaps not classified as
posts. Benevolent, Dominance, and Harrassment
misogyny at all. Neosexism is most closely related
are tagged in between only 45 and 70 posts.
to the definition “disrespectful actions, suggesting
or stating that women should be controlled in some
5.2 Domain/Sampling representation
way, especially by men”. This definition, however,
does not describe the direct denial that misogyny Most posts tagged as abusive and/or containing
exists. Without a distinct and explicit neosexism misogyny are retrieved from searches on posts from
category, however, these phenomena may be mixed public media profiles, see Table 3. Facebook and
up or even ignored. Twitter are equally represented, while Reddit is in
The taxonomy follows the suggestions of Vid- the minority. Reddit posts were sampled from an
gen et al. (2019) for establishing unifying tax- available historical collection.samp. domain dis. time abs. dis. dis. 2 annotators. IAA is calculated through average
dom in ⊂ ⊂ label agreement at post level – for example if two
k abus mis annotators label two posts [abusive, untargeted] and
topic Facebook 48% 07- 12,3 51% 63% [abusive, group targeted] the agreement would be
11/20 0.5. Our IAA during iterations of dataset construc-
keyw. Twitter 45% 08- 7,8 32% 27% tion ranged between 0.5 and 0.71. In the penulti-
12/20 mate annotation round we saw a drop in agreement
user Twitter 3,6 8% 6% (Figure 2); this is attributed to a change in underly-
keyw. Reddit 7% 02- 2,4 7% 2% ing text genre, moving to longer Reddit posts. 25%
04/19 of disagreements about classifications were solved
popul. Facebook 1 2% 2% during discussions. Annotators had the opportu-
nity to adjust their disagreed annotation in the first
Table 3: Distribution sampling techniques and domains revision individually, which represents the remain-
Sampling techniques: topic = posts from public media sites
and comments to these posts; keyw. = ing 75% (Table 5). The majority of disagreements
keyword/hashtag-search; popul. = most interactions. were on subtask A, deciding whether the post was
abusive or not.
5.3 Word Counts individual corr. group solv. discussion round
Frequencies of the words; ‘kvinder’ (women) and 417 169 69 (+125 pilot)
‘mænd’ (men) were the highest, but these words did
not represent strong polarities towards abusive and Table 5: Solved disagreements/flagged content
misogynistic content (Table 4). The word ‘user’
represents de-identified references to discussion The final overall Fleiss’ Kappa (Fleiss (1971))
participants (“@USER”). for individual subtasks are: abusive/not: 0.58, tar-
geted: 0.54, misogyny/not: 0.54. It is notable here
dataset ⊂ abus ⊂ mis that the dataset is significantly more skewed than
(kvinder, (kvinder, (kvinder, prior work which upsampled to 1:1 class balances.
0.29) 0.34) 0.41) Chance-corrected measurements are sensitive to
(user, 0.29) (user, 0.25) (mænd, 0.28) agreement on rare categories and higher agreement
(metoo, 0.25) (mænd, 0.22) (user, 0.18) is needed to reach reliability, as shown in Artstein
(mænd, 0.21) (bare, 0.17) (år, 0.16) and Poesio (2008).
(bare, 0.16) (metoo, 0.16) (når, 0.15)
5.5 Annotator disagreement analysis
Table 4: Top-3 word frequencies
tf-idf scores with prior removal of special character and stop- Based on the discussion rounds, the following types
words, notion:(token, tf-idf) of posts were the most challenging to annotate:
1. Interpretation of the author’s intention (irony,
5.4 Inter-Annotator Agreement (IAA) sarcasm, jokes, and questions)
E.g. Haha! Virksomheder i Danmark: Vi ansætter
1.0
per iteration aldrig en kvinde igen... (Haha! Companies in Den-
0.9 accumelated
0.8 mark: We will never hire a woman again ...)
0.7 0.71 0.7 0.7 0.71 sexisme og seksuelt frisind er da vist ikke det samme?
0.65
0.61
0.6 (I don’t believe sexism and sexual liberalism are the
0.5 0.46 same?)
0.4 0.4
0.3
2. Degree of abuse: Misrepresenting the truth to
0 5 10 15 20 25 30 harm the subject or fact
Annotated data samples in k
Figure 2: Inter-Annotator-Agreement E.g. Han er en stor løgner (He is a big liar)
y-axis: Agreement by rel. overlap of label-sequences per 3. Hashtags: Meaning and usage of hashtags in
sample; x-axis: Annotated data samples in k.
relation to the context
We measure IAA using the agreement between E.g. #nometoo
3 annotators for each instance until round 3 (7k 4. World knowledge required:
posts), and then sub-sampled data overlaps between Du siger at Frank bruger sin magt forkert men dubruger din til at brænde så mange mænd på bålet ... each experiment. Results are reported when the
(You say that Frank uses his power wrongly, but you use model reached stabilized per class f1 scores for
yours to throw so many men on the fire ... - referring to all classes on the test set (± 0.01/20). The results
a specific political topic.) indicate the expected challenge of accurately pre-
5. Quotes: re-posting or re-tweeting a quote dicting less-represented classes and generalizing to
gives limited information about the support or unseen data. Analysing False Positives and False
denial of the author Negatives on the misogyny detection task, we can-
not recognise noticeable correlations with other
6. Jargon: receiver’s perception
abusive forms and disagreements/ difficult cases
I skal alle have et klap i måsen herfra (You all get a
from the annotation task.
pat on the behind from me)
Handling these was an iterative process of raising subtask epoch f1 prec. recall
cases for revision in the discussion rounds, formu- abus/not 200 0.7650 76.43% 76.4%
lating the issue, and providing documentation. We target 120 0.6502 64.45% 66.2%
added the status and, where applicable, outcome misog./not 200 0.8549 85.27% 85.85%
from these cases to the guidelines. We also added misog.* 0.6191
explanations of hashtags and definitions of unclear misog.categ. 100 0.7913 77.79% 81.26%
identities, like “the media”, as a company. For Table 6: Baseline Evaluation: F1-scores, Precision, Re-
quotes without declaration of rejection or support, call (weighted, *except for misog., class f1-score) with
we agreed to label them as not abusive, since the mBERT
motivation of re-posting is not clear.
5.6 Baseline Experiments as an indicator 6 Discussion and reflection
Lastly, we provide a classification baseline: For
misogyny and abusive language, the BERT model Reflections on sampling We sampled from dif-
from Devlin et al. (2019) proved to be a robust ar- ferent platforms, and applied different sampling
chitecture for cross-domain (Swamy et al., 2019) techniques. The goal was to ensure, first, a suf-
and cross-lingual (Pamungkas et al., 2020; Mulki ficient amount of misogynistic content and, sec-
and Ghanem, 2021) transfer. We use therefore mul- ondly, mitigation of biases stemming from a uni-
tilingual BERT (’bert-base-multilingual-un cased’) form dataset.
for general language understanding in Danish, fine- Surprisingly, topic sampling unearthed a higher
tuned on our dataset. density of misogynistic content than targeted key-
Model: We follow the suggested parameters word search (Table 3). While researching plat-
from Mosbach et al. (2020) for fine-tuning (learn- forms, we noticed the limited presence of Dan-
ing rate 2e-5, weight decay 0.01, AdamW opti- ish for publicly available men-dominated fora
mizer without bias correction). Class imbalance is (e.g. gaming forums such as DotA2 and extrem-
handled by weighted sampling and data split for ist plaftorms such as Gab (Kennedy et al., 2018)).
train/test 80/20. Experiments are conducted with This, as well as limitations of platform APIs caused
batch size 32 using Tesla V100 GPU. a narrow data selection. Often, non-privileged lan-
Preprocessing: Our initial pre-processing of the guages can gain from cross-language transfer learn-
unstrucutured posts included converting emojis to ing. We experimented with translating misogy-
text, url replacement, limit @USER and punctu- nistic posts from Fersini et al. (2018) to Danish,
ation occurrences and adding special tokens for using translation services, and thereby augment the
upper case letters adopted from Ahn et al. (2020). minority class data. Translation services did not
Classification: Since the effect of applying multi- provide a sampling alternative. Additionally, as
task-learning might not conditionally improve per- discovered by Anzovino et al. (2018), misogynis-
formance (Mulki and Ghanem, 2021), the classi- tic content seems to vary with culture. This makes
fication is evaluated on a subset of the dataset for
each subtask (see Table 6) including all posts of the total text corrected label corrected out
target label (e.g. misogyny) and stratified sampling 960 877 224 48
of the non-target classes (e.g. for non-misogynistic:
abusive and non-abusive posts) with 10k posts for Table 7: Translating IberEval posts EN to DAlanguage-specific investigations important, both for 7 Conclusion and future work
the sake of quality of automatic detection systems,
In this work, we have documented the construction
as well as for cultural discovery and investigation.
of a dataset for training systems for automatic de-
Table 7 shows results of post-translation manual
tection of online misogyny. We also present the
correction by annotators (all fluent in English).
resulting dataset of misogyny in Danish social me-
dia, Bajer, including class balance, word counts,
Reflections on annotation process Using just and baseline as an indicator. This dataset is avail-
seven annotators has the disadvantage that one is able for research purposes upon request.
unlikely to achieve as broad a range of annotator The objective of this research was to explore the
profiles as, for instance, through crowdsourcing. design of an annotation process which would result
However, during annotation and weekly discus- in a high quality dataset, and which was transparent
sions, we saw clear benefits from having a small and useful for other researchers.
annotator group with different backgrounds and Our approach was to recruit and train a diverse
intense training. While annotation quality cannot group of annotators and build a taxonomy and code-
be measured by IAA alone, the time for debate clar- book through collaborative and iterative annotator-
ified taxonomy items, gave thorough guidelines, involved discussions. The annotators reached good
and increased the likelihood of correct annotations. agreement, indicating that the taxonomy and code-
The latter reflects the quality of the final dataset, book were understandable and useful.
while the former two indicate that the taxonomy However, to rigorously evaluate the quality of
and codebook are likely useful for other researchers the dataset and the performance of models that
analysing and processing online misogyny. build on it, the models should be evaluated in prac-
tice with different text types and languages, as well
6.1 A comprehensive taxonomy for misogyny as compared and combined with models trained
on different datasets, i.e. Guest et al. (2021). Be-
The semi-open development of the taxonomy and cause online misogyny is a sensitive and precarious
frequent discussions allowed the detection neo- subject, we also propose that the performance of
sexism as an implicit form of misogyny. Future automatic detection models should be evaluated
research in taxonomies of misogyny could con- with use of qualitative methods (Inie and Derczyn-
sider including distinctions between active/passive ski, 2021), bringing humans into the loop. As we
misogyny, as suggested by Anzovino et al. (2018) found through our continuous discussions, online
as well as other sub-phenomena. abuse can present in surprising forms, for instance
In the resulting dataset, we saw a strong repre- the denial that misogyny exists. The necessary in-
sentation of neosexism. Whether this is a specific tegration of knowledge and concepts from relevant
cultural phenomenon for Danish, or indicative of fields, e.g. social science, into NLP research is only
general online behaviour, is not clear. really possible through thorough human participa-
The use of unified taxonomies in research af- tion and discussion.
fords the possibility to test the codebook guide-
Acknowledgement
lines iteratively. We include a short version of the
guidelines in the appendix; the original document This research was supported by the IT Univer-
consists of seventeen pages. In a feedback survey sity of Copenhagen, Computer Science for inter-
following the annotation work, most of the anno- nal funding on Abusive Language Detection; and
tators described that during the process, they used the Independent Research Fund Denmark under
the guidelines primarily for revision in case they project 9131-00131B, Verif-AI. We thank our anno-
felt unsure how to label the post. To make the tators Nina Schøler Nørgaard, Tamana Saidi, Jonas
annotation more intuitively clear for annotators, Joachim Kofoed, Freja Birk, Cecilia Andersen, Ul-
we suggest reconsidering documentation tools and rik Dolzyk, Im Sofie Skak and Rania M. Tawfik.
their accessibility for annotators. Guidelines are We are also grateful for discussions with Debora
crucial for handling linguistic challenges, and well- Nozza, Elisabetta Fersini and Tracie Farrell.
documented decisions about them serve to create
comparable research on detecting online misogyny
across languages and dataset.Impact statement: Data anonymization In Proceedings of the Fourth Workshop on Online
Abuse and Harms, pages 125–137, Online. Associa-
Usernames and discussion participant/author tion for Computational Linguistics.
names are replaced with a token @USER value.
Valerio Basile, Cristina Bosco, Elisabetta Fersini,
Annotators were presented with the text of the post
Debora Nozza, Viviana Patti, Francisco Manuel
and no author information. Posts that could not Rangel Pardo, Paolo Rosso, and Manuela San-
be interpreted by annotators because of missing guinetti. 2019. SemEval-2019 Task 5: Multilingual
background information were excluded. We only Detection of Hate Speech Against Immigrants and
gathered public posts. Women in Twitter. In Proceedings of the 13th Inter-
national Workshop on Semantic Evaluation, pages
Annotators worked in a tool where they could 54–63, Minneapolis, Minnesota, USA. Association
not export or copy data. Annotators are instructed for Computational Linguistics.
to flag and skip PII-bearing posts.
Emily M. Bender and Batya Friedman. 2018. Data
All further information about dataset creation is
Statements for Natural Language Processing: To-
included in the main body of the paper above. ward Mitigating System Bias and Enabling Better
Science. Transactions of the Association for Com-
putational Linguistics, 6:587–604.
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