ARCOV-19: THE FIRST ARABIC COVID-19 TWITTER DATASET WITH PROPAGATION NETWORKS
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ArCOV-19: The First Arabic COVID-19 Twitter Dataset
with Propagation Networks
Fatima Haouari, Maram Hasanain, Reem Suwaileh, Tamer Elsayed
Computer Science and Engineering Department, Qatar University
{200159617, maram.hasanain, rs081123, telsayed}@qu.edu.qa
Abstract Since the first reported case of Novel Coron-
avirus (COVID-19) in China, in November 2019,
In this paper, we present ArCOV-19, an Arabic
COVID-19 Twitter dataset that spans one year, the COVID-19 topic has drawn the interest of many
covering the period from 27th of January 2020 Arab users over Twitter. Their interest, reflected
till 31st of January 2021. ArCOV-19 is the first in the Arabic content on the platform, has reached
publicly-available Arabic Twitter dataset cov- a peak after two months when the first case was
ering COVID-19 pandemic that includes about reported in the United Arab Emirates late in Jan-
2.7M tweets alongside the propagation net- uary 2020. This ongoing pandemic has, unsur-
works of the most-popular subset of them (i.e.,
prisingly, spiked discussions on Twitter covering a
most-retweeted and -liked). The propagation
networks include both retweets and conversa-
wide range of topics such as general information
tional threads (i.e., threads of replies). ArCOV- about the disease, preventive measures, procedures
19 is designed to enable research under several and newly-enforced decisions by governments, up-
domains including natural language process- to-date statistics of the spread in the world, and
ing, information retrieval, and social comput- even the change in our daily habits and work styles.
ing. Preliminary analysis shows that ArCOV- In this work, we aim to support future research
19 captures rising discussions associated with on social media during this historical period of our
the first reported cases of the disease as they
time by curating an Arabic dataset (ArCOV-19)
appeared in the Arab world. In addition to the
source tweets and propagation networks, we that exclusively covers tweets about COVID-19.
also release the search queries and language- We limit the dataset to Arabic since it is among
independent crawler used to collect the tweets the most dominant languages in Twitter (Alshaabi
to encourage the curation of similar datasets. et al., 2020a), yet under-studied in general.
ArCOV-19 is the first Arabic Twitter dataset
1 Introduction designed to capture tweets discussing COVID-19
Twitter streams hundreds of millions of tweets starting from January 27th 2020 till end of January
daily. In addition to being a medium for the spread 20211 , constituting about 2.7M tweets, alongside
and consumption of news, it has been shown to the propagation networks of the most-popular sub-
capture the dynamics of real-world events includ- set of them. To our knowledge, there is no publicly-
ing the spread of diseases such as the seasonal available Arabic Twitter dataset for COVID-19 that
influenza (Kagashe et al., 2017) or more severe includes the propagation networks of a good subset
epidemics like Zika (Vijaykumar et al., 2018), of its tweets. Some existing efforts have already
Ebola (Roy et al., 2020), and H1N1 (McNeill et al., started to curate COVID-19 datasets including Ara-
2016). Moreover, collective conversations on Twit- bic tweets, but Arabic is severely under-represented
ter about an event can have a great influence on the (e.g., (Chen et al., 2020a; Singh et al., 2020)) or
event’s outcomes, e.g., US 2016 presidential elec- represented by a random sample that is not specifi-
tions (Grover et al., 2019). Analyzing tweets about cally focused on COVID-19 (e.g., (Alshaabi et al.,
an event, as it evolves, offers a great opportunity 2020b)), or the dataset is limited in the period it
to understanding its structure and characteristics, covers and does not include the propagation net-
informing decisions based on its development, and works (e.g., (Alqurashi et al., 2020)).
anticipating its outcomes as represented in Twitter 1
The dataset will be continuously augmented with new
and, more importantly, in the real world. tweets over the coming months.
82
Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 82–91
Kyiv, Ukraine (Virtual), April 19, 2021.The contribution of this paper is three-fold: constituting 65% and 12% of the tweets respec-
tively. Chen et al. (2020b) are continously collect-
• We release ArCOV-19,2 the first Arabic Twit- ing tweets since 21st of January 2020, where at
ter dataset about COVID-19 that comprises the time of writing, around 66% and 11% of the
about 2.7M tweets collected via Twitter search tweets are in English and Spanish respectively. An-
API. It covers a full year allowing for captur- other multilingual ongoing collection is released
ing discussions on the topic since it started to by Lopez et al. (2020). Qazi et al. (2020) enriched
be popular in the Arab world. In addition to their large-scale multilingual Twitter dataset with
the tweets, ArCOV-19 includes propagation geolocation information.
networks of the most popular subset, search The only raw Arabic Twitter dataset available is
queries, and documented implementation of the one released by Alqurashi et al. (2020), how-
our language-independent tweets crawler. ever it covers the period from 1st of January to 15th
• We present a preliminary analysis on ArCOV- April 2020 only. Moreover, it does not include the
19, which reveals insights from the dataset propagation networks of tweets.
concerning temporal, geographical, and topi- Compared to existing datasets, we release an
cal aspects. only-Arabic collection of tweets, where we exclude
• We suggest several use cases to enable re- retweets to avoid having copies of the same tweet.
search on Arabic tweets in different research Moreover, we also release the most-popular sub-
areas including, but not limited to, emergency set of them (i.e., most-retweeted and -liked and
management, misinformation detection, and spam free tweets). Furthermore, we collect the
social analytics. propagation networks including both retweets and
conversational threads (i.e., threads of replies) for
The reminder of the paper is organized as fol- the most-popular subset.
lows. We present the crawling process followed
to acquire ArCOV-19 in Section 3. We then thor- 3 Data Collection
oughly discuss the analysis that we conducted on
the dataset in Section 4. We suggest some use cases ArCOV-19 includes two major components: the
to enable research on Arabic tweets in Section 5. source tweets (i.e., tweets collected via Twitter
We finally conclude in Section 6. search API every day) and the propagation net-
works (i.e., retweets and conversational threads
2 Related Work of a subset of the source tweets). In this section,
we present how we collected each in Sections 3.1,
Social media platforms and Twitter specifically and 3.2 respectively, and summarize the released
showed to be an indispensable medium for sharing data in Section 3.3.
information and discussions about the COVID-19
pandemic since December 2019. A considerable 3.1 Tweets Collection
body of raw social media datasets were released To collect the source tweets, we used our tweet
to facilitate analyzing these discussions including crawler3 that uses Twitter search API4 . The crawler
Twitter datasets (Banda et al., 2020; Lopez et al., takes a set of manually-crafted queries, comprising
2020; Chen et al., 2020b; Qazi et al., 2020; Gao a target topic, as input. At the end of each day, the
et al., 2020; Alqurashi et al., 2020; Dashtian and crawler issues a search request for each of those
Murthy, 2021), Instagram (Zarei et al., 2020), or queries. Queries can be keywords (e.g., “Corona”),
the Chinese social media platform Weibo (Hu et al., phrases (e.g., “the killing virus”), or hashtags (e.g.,
2020; Gao et al., 2020). “#the new coronavirus”). Twitter returns a maxi-
Banda et al. (2020) released a multilingual mum of 3,200 tweets per query. We customized
dataset that includes over 800M tweets, the data is the search requests to return only Arabic tweets,5
collected since 1st of January 2020 using Twitter and to exclude all retweets to avoid having copies
streaming API. Dashtian and Murthy (2021), col-
3
lected multilingual tweets posted between March https://gitlab.com/bigirqu/ArCOV-19/
-/tree/master/code/crawler
and July 2020. The most dominant languages 4
https://developer.twitter.com/en/
covered by their dataset are English and Spanish docs/tweets/search/api-reference/
get-search-tweets
2 5
https://gitlab.com/bigirqu/ArCOV-19/ Language of tweets to return is a configurable parameter.
83of the same tweet. Additionally, duplicate tweets retweets using Pickaw,8 a platform for organiz-
(returned by different queries) are removed in each ing contests on social media, and the replies using
day. Finally, tweets are sorted chronologically. PHEME (Zubiaga et al., 2016) Twitter conversation
We started collecting our data since 27th of Jan- collection script.9
uary 2020 using a set of queries that we manually-
updated based on our daily tracking of trending 3.3 Data Release
keywords and hashtags. For example, starting from In summary, we release the following resources
16th of December 2020, we extended our queries as ArCOV-19 dataset, taking into consideration
to cover tweets related to COVID-19 vaccines as Twitter content redistribution policy:10
that sub-topic was gaining interest on Arabic social • Source Tweets: IDs of the tweets crawled in
media. The full list of queries used in each day each day.
is released alongside our dataset. We denote all • Search Queries: the list of search queries, in-
collected tweets as source tweets. cluding keywords, phrases, and hashtags, used
Due to technical reasons, we missed collecting in each period to collect our source tweets.
tweets for a few days. Since Twitter search API • Top Subset: IDs of the top 1,000 most popu-
limits the search results to the past 7 days, we lar tweets for each day.
missed the old tweets. To overcome that, we used • Propagation Networks: the propagation net-
GetOldTweets36 python library to download the works for the top subset which include for
search results using the same trending keywords each tweet in the top subset:
and hashtags we selected around those days. – Retweets: IDs of the full retweet set.
– Conversational Threads: tweet IDs of
3.2 Propagation Networks Collection
the full reply thread (including direct and
In addition to the source tweets, we also collected indirect replies).
the propagation networks (i.e., retweets and con- Along with the dataset, we provide some pointers
versational threads) of the top 1000 most popular to publicly-available crawlers that users can easily
(i.e., top-retweeted & -liked) tweets each day. To use to crawl the tweets given their IDs.
our knowledge, this is the first Arabic tweet dataset
to include such propagation networks. 4 ArCOV-19 in Numbers
Before getting the most popular tweets on any
In this section, we present a statistical summary and
day, we started from source tweets collected in
conduct an analysis on ArCOV-19 to shed some
that day and applied a qualification pipeline. We
light on its major characteristics.
first excluded tweets containing any inappropriate
word (using a list of inappropriate words we con- 4.1 Tweets and Users Distribution
structed). Next, tweets with more than two URLs
or four hashtags, or shorter than four tokens (all Table 1 presents an overall summary of the tweets
are potentially spam) were dropped. Additionally, and users statistics in ArCOV-19. It indicates that
duplicate tweets that have exact textual content are the total number of source tweets in the dataset
also dropped to avoid redundancy; only the most is about 2.7M posted by over 690k unique users.
popular of them (according to our scoring criterion We note that 18.66% of the tweets were posted by
below) is kept. Qualified tweets are then scored by verified users (who constitute only 0.81% of the
popularity defined by the sum of the tweet’s retweet unique users). This is a relatively large percentage,
and favorite counts. We finally sort the qualified showing that a good portion of the source tweets
tweets by their scores and select the most popular are popular. The average numbers of followers,
1,000 tweets. We denote the set of all such tweets friends, and statuses of users are also relatively
over all days as the top subset. large, showing that users observed in ArCOV-19
For those 1K top tweets per day, we then col- are also popular (and possibly more influential).
lected all retweets and conversational threads (i.e., rest; we will make all available in the near future.
direct and indirect replies).7 We collected the 8
https://pickaw.com/en/
twrench-becomes-pickaw
6 9
https://github.com/Mottl/ https://github.com/azubiaga/
GetOldTweets3 pheme-twitter-conversation-collection
7 10
At the time of writing, we have collected the replies for https://developer.twitter.com/en/
tweets until end of April 2020 and we are still collecting the developer-terms/agreement-and-policy
84550,000
The table also indicates that 25.40% of the tweets 500,000
Posted by non-verified users Posted by verified users
include URLs. We anticipate that this is due to the 450,000
400,000
extensive spreading of news (linked from tweets) 350,000
during this period. The numbers of tweets that are 300,000
250,000
geotagged and geolocated are also indicated in the 200,000
table; however, we defer the discussion of such 150,000
100,000
types of tweets to Section 4.3. 50,000
Figure 1 illustrates the monthly distribution of 0
Dec 2020
Mar 2020
May 2020
Apr 2020
Nov 2020
Aug 2020
Jan 2020
Sep 2020
Jan 2021
Oct 2020
Jul 2020
Jun 2020
Feb 2020
tweets in ArCOV-19. The volume of tweets dras-
tically increased in March when the virus started
to spread in several Arab countries. Then, tweets Figure 1: Monthly distribution of source tweets of
number started to decrease monthly. We speculate ArCOV-19.
this is due to the fact that the topic was not heavily
Akhbar | أﺧﺑﺎر اﻵن 11,086
discussed as in the first days. In December, we ﺻﺣﯾﻔﺔ اﻟﺑﯾﺎن 10,223
Shorouk News 9,031
expanded our queries list to include hashtags and Bawabaa News | ﺑواﺑﺔ اﻷﺧﺑﺎر 8,453
Assawsana News 6,962
keyword related to more current sub-topics such as اﻟﯾوم اﻟﺳﺎﺑﻊ
ﺑواﺑﺔ اﻟوطن
6,224
5,810
vaccines and the new variant of the virus, which NEWS KTV
ﻛوروﻧﺎ 5,517
5,263
resulted in an increase in the volume of tweets. Mulhak
ﻗﻧﺎة اﻟﻐد
5,019
4,953
AlMamlakaTV 4,890
Figure 2 presents the top 25 tweeting users in ﺻﺣﯾﻔﺔ اﻟﺧﻠﯾﺞ 4,844
ALAYAM - اﻷﯾﺎم 4,699
ArCOV-19. We notice that several of them are news 24اﻟﺷﺎرﻗﺔ 4,584
ﺟرﯾدة اﻟدﺳﺗور 4,536
sources, and 20 are verified Twitter accounts. MENAFN.com Arabic
ﺳﻛﺎي ﻧﯾوز ﻋرﺑﯾﺔ
4,137
4,124
اﻟﻌرﺑﻲ اﻟﺟدﯾد 4,068
ﺻﺣﯾﻔﺔ اﻻﺗﺣﺎد 4,051
Tweets Statistics ﺟرﯾدة اﻟوطن
Independent ﻋرﺑﯾﺔ
4,021
3,989
ﺻدى اﻟﺑﻠد 3,975
Source Tweets 2,675,049 اﻟوﻗﺎﺋﻊ اﻻﺧﺑﺎرﯾﺔ
ﺻﺣﯾﻔﺔ ﻋﻛﺎظ
3,968
3,912
Geotagged 2,078 (0.08%) 0 2,000 4,000 6,000 8,000 10,000 12,000
Number of tweets
Geolocated 60,873 (2.28%)
Posted by verified users 499,351 (18.66%) Figure 2: Number of tweets posted by the top 25 tweet-
Include URL 679,482 (25.40%) ers in ArCOV-19 for the period of 27th Jan 2020 to 31st
Include Media 973,952 (36.41%) Jan 2021.
Top Subset 370,132 (13.84%)
Retweets of Top Subset 7,925,821
Replies of Top Subset 1,476,950 country names for the tweets posted in the period
of 27th Jan to 31st Mar 2020. We focus on this
Users Statistics critical period since the virus started to spread in
Unique 690,339 the Arab world during that time. We then tracked
Verified 5,575 (0.81%) their frequency over time. Figure 3 shows the time
Average followers count 4,630 series (over days) of the three types of keywords.11
Average friends count 918 The 10 most-frequent words shown in Fig-
Average statuses count 9,054 ure 3(a) indicate two different types of words:
those that are directly related to COVID-19 (e.g.,
Table 1: Tweets and users statistics of ArCOV-19. Stats
“health”) and those that are not but related to prayers
for replies are for tweets until end of April 2020.
and supplications (e.g., “Allah/God”)). It is inter-
esting to see the word “Allah” is very frequent
4.2 Tweets Content & Topics early on when the news about the virus started to
It is important to demonstrate that the tweets in spread (probably over discussions around whether
ArCOV-19 constitute a good representative sample the pandemic is a punishment from God or not, we
of the Arabic tweets posted during the target period believe), then declines over time only to become
on COVID-19, and that they cover the prevalent frequent again when the virus started to widely
topics discussed over Twitter during that period. spread in the Arab world.
To help examine this hypothesis, we analyzed the 11
When identifying the most frequent words and hashtags,
textual content of the tweets; in particular, we iden- we excluded the ones we used in our search queries since they
tified the most-frequent words, hashtags, and Arab are expected to be very frequent by definition.
8530%
هللا هللا حالة بفيروس الصحة بسبب
25% اصابة العالم انتشار اللهم عدد
اصابة
20% بفيروس
حالة
% tweets
الصحة بسبب
15%
اللهم انتشار
10%
العالم
5%
0% عدد
01-Mar
03-Mar
05-Mar
07-Mar
09-Mar
11-Mar
13-Mar
15-Mar
17-Mar
19-Mar
21-Mar
23-Mar
25-Mar
27-Mar
29-Mar
31-Mar
27-Jan
29-Jan
31-Jan
02-Feb
04-Feb
06-Feb
08-Feb
10-Feb
12-Feb
14-Feb
16-Feb
18-Feb
20-Feb
22-Feb
24-Feb
26-Feb
28-Feb
(a) Top 10 most frequent words
20%
_كورونا# #الكويت #الصين
الكويت _كورونا# #إيران #عاجل
الصين#
_كورونا# في_الكويت #كورونا_في_الكويت #السعودية
15%
لبنان #كورونا_الكويت #كورونا_لبنان
#لبنان #covid19
لبنان#
% tweets
الكويت# عاجل#
10%
إيران#
#covid19
5%
السعودية#
0%
11-Mar
13-Mar
15-Mar
17-Mar
19-Mar
21-Mar
23-Mar
25-Mar
27-Mar
29-Mar
31-Mar
2-Feb
4-Feb
6-Feb
8-Feb
27-Jan
29-Jan
31-Jan
10-Feb
12-Feb
14-Feb
16-Feb
18-Feb
20-Feb
22-Feb
24-Feb
26-Feb
28-Feb
1-Mar
3-Mar
5-Mar
7-Mar
9-Mar
(b) Top 10 most frequent hashtags
Time of first reported infected case
ليبيا
مصر
لبنان
الكويت عمان البحرين العراق
قطر
السعودية تونس المغرب األردن
سوريا
اإلمارات
الجزائر
السودان موريتانيا
فلسطين
12%
مصر لبنان الكويت الكويت مصر لبنان السعودية
االمارات العراق البحرين قطر
10% البحرين
العراق اليمن االردن الجزائر سوريا
8% المغرب السودان تونس فلسطين
% tweets
ليبيا عمان موريتانيا الصومال
6% الجزائر
اإلمارات عمان
4% السعودية قطر
ليبيا
2%
0%
17-Mar
31-Mar
01-Mar
03-Mar
05-Mar
07-Mar
09-Mar
11-Mar
13-Mar
15-Mar
19-Mar
21-Mar
23-Mar
25-Mar
27-Mar
29-Mar
27-Jan
29-Jan
31-Jan
02-Feb
04-Feb
06-Feb
08-Feb
10-Feb
12-Feb
14-Feb
16-Feb
18-Feb
20-Feb
22-Feb
24-Feb
26-Feb
28-Feb
(c) Word frequency of Arab countries
Figure 3: Time series of the frequency (in percentage of tweets) of top general keywords, hashtags, and Arab
country words for the period of 27th Jan to 31st Mar. The time of first reported cases in Arab countries is also
indicated and aligned with the time series.
86youm7.com 5420
Figure 3(b) demonstrates how “#China” hashtag alarabiya.net
youtube.com 2984
3405
elwatannews.com
was the most trending one from 27 of January until alaraby.co.uk
2769
2569
aawsat.com 2348
20 of February, as COVID-19 was prevalent only sabq.org
independentarabia.com
2342
2143
in China and still not widely spread (at that time) in shorouknews.com
cnn.com
2041
1989
akhbaralaan.net 1944
other countries. We can see how it started to be less rt.com
alittihad.ae
1913
1901
trending as the number of cases started to decline skynewsarabia.com
argaam.com
1792
1705
ajel.sa 1432
in China by that date.12 On the other hand, when royanews.tv
aa.com.tr
1405
1379
COVID-19 started to spread in the Arab world, ow.ly
euronews.com
1346
1306
24.ae 1239
other hashtags started to become viral. Further- ampproject.org
al-ain.com
1231
1218
more, Figure 3(b) shows that frequency spikes of pscp.tv
forbesmiddleeast.com
1200
1185
“#Iran”, “#Lebanon”, and “#Kuwait” exactly match 0 1000 2000 3000 4000 5000 6000
Number of occurrences
the confirmation dates of first reported cases in
Iran,13 Lebanon,14 and Kuwait,15 respectively. Figure 4: Most frequently-linked domains in the top
To further analyze trending topics, Figure 3 fea- tweets subset for the period of 27th Jan 2020 to 31st
tures the timeline of the first reported cases in the Jan 2021.
Arab countries and aligns them with the time se-
ries throughout the figure. Aligning the timeline ArCOV-19 by considering the domains of URLs
with the series in Figure 3(c) reveals a significant shared in the tweets. We focused on the top tweets
match between the frequency peaks of several coun- subset of ArCOV-19 (constructed as detailed in Sec-
try names and the corresponding dates of first re- tion 3.2) and identified the URLs posted in those
ported cases in those countries, most notably in tweets. We then expanded them (since Twitter
UAE, Egypt, Lebanon, Kuwait, Oman, Bahrain, URLs are shortened) and dropped URLs of images
Algeria, and Libya. and videos uploaded to Twitter, to focus only on
Figure 3 also demonstrates the power of ArCOV- URLs referring to external sources. We extracted
19 in capturing further controversial and trending 110,220 URLs from 2,617 unique domains. Fig-
topics. Table 2 shows a timeline covering dates ure 4 shows that URLs from news websites are
of specific topics of discussion trending on social dominantly the most-commonly shared. We ob-
media around times of peaks in tweeting frequency serve that these news websites mainly originate
in Figure 3(c). from three Arab countries, namely, Egypt, Saudi
Country Date Topic Related News Arabia, and United Arab Emirates. Interestingly,
UAE Feb 19 Yemeni Foreign http://tiny.cc/71qtmz videos from YouTube are among the most com-
Affairs Minister
thanks UAE for
monly shared media. Coupled with the fact that
evacuating Yemeni 36% of the tweets in ArCOV-19 include embedded
students from
China
images and videos (Table 1), we believe this en-
Iraq Feb 20 Iraq announces http://tiny.cc/yzrtmz ables ArCOV-19 to be a potential dataset to further
closure of borders
with Iran
support the evaluation of multi-modal retrieval and
Egypt Mar 2 Egyptian health http://tiny.cc/7iqtmz classification systems.
minister announces
a visit to China
Mar 7 Kuwait bans travels http://tiny.cc/lqstmz 4.3 Geographic Distribution of Tweets
with Egypt
including entry of Although Twitter provides automatic geo-reference
Egyptian residents functionally, few users (solely around 1-3% (Mur-
KSA Mar 4-5 Closure of The http://tiny.cc/iqrtmz
Grand Mosque in dock, 2011; Jurgens et al., 2015)) opt to enable it
Mecca due to privacy and safety reasons. Alternatively, to
have an insight about the geographical distribution
Table 2: Examples of trending topics in social media
matched by spikes in tweeting frequency in ArCOV-19 and diversity of the tweets in our dataset, we ex-
amined the place and coordinates attributes of the
We further explore the topics discussed in Tweet object.16 We note that the place attribute is
an optional attribute that allows the user to select a
12
https://tinyurl.com/rfj5khn
13 16
https://tinyurl.com/yykgwbox https://developer.twitter.com/en/
14
https://tinyurl.com/yxkg78g5 docs/tweets/data-dictionary/overview/
15
https://tinyurl.com/y4nlgz5h geo-objects
87105
location from a list provided by Twitter (therefore,
the location might not necessarily show the actual 104
location from where the tweet is posted), whereas
Number of retweets
the coordinates attribute represents the geographic 103
location of the tweet as reported by the user or
client application. 102
Table 1 shows that ArCOV-19 has 60,873 ge-
101
olocated tweets (i.e., having values in the place
attribute) and 2,078 geotagged tweets (i.e., having 100
values in the coordinates attribute). Those tweets
27/01
29/01
31/01
02/02
04/02
06/02
08/02
10/02
12/02
14/02
16/02
18/02
20/02
22/02
24/02
26/02
28/02
01/03
03/03
05/03
07/03
09/03
11/03
13/03
15/03
17/03
19/03
21/03
23/03
25/03
27/03
29/03
31/03
02/04
04/04
06/04
08/04
10/04
12/04
14/04
16/04
18/04
20/04
22/04
24/04
26/04
28/04
30/04
were posted by 24,072 and 256 unique users re-
spectively. The geolocated tweets constitute about Figure 6: Distribution of retweets per day for the top
2.28% of the total source tweets, which is con- subset for the period of 27th Jan 2020 to 30th April
sistent with previous studies (Huang and Carley, 2020.
2019).
among the tracking keywords in the period between
Libya
1.7%
Bahrain
22 of February and 13 of March. Furthermore, it is
1.9%
Qatar
not surprising to see the countries that have a few
2.1%
Jordan
cases have the smallest portions of contribution to
3.0%
Iraq KSA the content. Others have small audience userbases
4.2% 41.7%
Lebanon on Twitter (e.g., Tunisia).
5.3%
Egypt
5.4% 4.4 Propagation Networks
Oman
6.6% As discussed earlier, we collected the propagation
UAE
networks of the top subset. Overall, the number of
6.8% retweets is 7,925,821 for the entire subset, and the
Other Kuwait
7.3% 9.6% number of replies is 1,476,950 18 .
At the time of collecting the retweets, we were
Figure 5: Distribution of geolocated tweets for the pe- not able to get the retweets of some of the tweets ei-
riod of 27th Jan 2020 to 31st Jan 2021. ther because they were deleted or they were posted
from private accounts. For those collected suc-
The geolocated tweets were indeed posted by cessfully, Figure 6 illustrates the distribution of
users from 102 countries from around the globe. retweets per day using boxplots. It shows that the
We found that 92.75% of them were posted from average (and median) number of retweets follows
the Arab world. The largest contribution of the ge- a similar pattern to what was shown in Figure 1
olocated content (about 41.7%) comes from Saudi (notice that the Y-axis here is in log scale). We also
Arabia; this is somewhat expected as Saudi users notice that a good number of tweets got more than
represent the highest number of active Twitter users 100 retweets; some of them got even larger num-
in the Arab world.17 Surprisingly, Kuwait comes bers reaching about 10k retweets or more, showing
second with 9.6% of the geolocated content. We highly propagated content. As shown in Figure 7,
believe the rationale is that it was among the first we applied a similar analysis to the replies per day,
countries that reported COVID-19 cases in the Mid- and found similar patterns to the retweets.
dle East. Since then, Kuwait started a series of strict
precautions such as a wide lock-down in many vi- 5 Enabling Research
tal facilities until the government had imposed a
With the spread of Novel Coronavirus in several
nationwide curfew. We think, after the curfew, the
Arab countries and the subsequent procedural mea-
people become more active on Twitter as a plat-
sures taken by the local governments, it continued
form to break news and discuss developments of
to dominate discussions over social media (and
the virus. Additionally, we used related phrases
18
and hashtags to “Kuwait”, “Lebanon”, and “UAE” The stats are for the replies for data until end of April
2020 and we are still collecting the rest; we will make all
17
https://tinyurl.com/jmkttt3 available in the near future.
88Misinformation Detection: With the sheer
104
amount of information shared about COVID-19,
many rumors are disseminated and getting high at-
103
Number of replies
tention from the community, which causes a fast
propagation of misinformation. This hinders the
102
efforts of the health and governmental organiza-
101
tions on fighting the pandemic as such rumors
spread panic and may lead to undesirable conse-
100
quences (e.g., increase of cases and mortality rate,
or lack of supplies due to hoarding). ArCOV-19
27/01
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27/03
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30/04
supports studying information/claims propagation,
Figure 7: Distribution of replies per day for the top sub- claims check-worthiness detection and verification
set for the period of 27th Jan 2020 to 30th April 2020. tasks on the most popular tweets. Furthermore,
the retweet networks and conversational threads
provide a valuable resource for early detection of
Twitter in particular) to the time of this writing. To fake news and identification of malicious rumor-
enable research on different tasks on Arabic Tweets, spreading accounts. To further facilitate work in
we make ArCOV-19 publicly-available for the re- this domain of problems, we recently constructed
search community. Having quantified the topical, ArCOV19-Rumors, an annotated dataset on top of
geographical, and community representativeness ArCOV-19 to support claim and tweet verification
of ArCOV-19, we envision that it is suitable for (Haouari et al., 2021).
diverse natural language processing, information
retrieval, and computational social science research 6 Conclusion
tasks including, but not limited to, emergency man-
agement, misinformation detection, and social ana- In this paper, we presented ArCOV-19, the first
lytics, as we discuss below. Furthermore, we pro- Arabic Twitter dataset about the Novel Coronavirus
vide propagation networks of daily highly popular (COVID-19) that includes propagation networks
subset (the most popular 1K) to ensure the quality of a large subset of tweets. We release all source
of tweets. This sample is drawn after filtering out tweets, top subset, search queries, and the propa-
potential low-quality tweets (e.g., spam) and con- gation networks. Preliminary analysis showed that
tent duplicates. We anticipate the top subset and ArCOV-19 captured spikes in tweeting frequency
the network to support these tasks on the popular for country-specific tweets that are consistent with
Arabic tweets that we assume have the most effect the first reported cases of COVID-19 in several
on shaping public opinion and understanding. Arab countries. We also found dominance of news
Emergency Management: As COVID-19 is an agencies among top tweeting users and among most
international pandemic, national and international shared URLs. ArCOV-19 enables research under
health organizations need to analyze the effects of many domains including natural language process-
the outbreak. We believe ArCOV-19 can support ing, information retrieval, and social computing.
several tasks in that domain, such as filtering of in- We plan to continue collecting tweets for the fore-
formative content, events and sub-events detection, seeable future and the dataset will be continuously
summarization, identification of eyewitnesses, ge- updated with newly collected tweets and propaga-
olocation, and studying information and situational tion networks.
awareness propagation, to name a few.
Acknowledgments
Social Analytics: In addition to sharing reports
and awareness during the outbreak, people tend to The work of Tamer Elsayed and Maram Hasanain
discuss their opinions (e.g., stance towards the situ- was made possible by NPRP grant# NPRP 11S-
ation and its consequences) and express their emo- 1204-170060 from the Qatar National Research
tions (e.g., big changes in lifestyle, social distanc- Fund (a member of Qatar Foundation). The work
ing, loss of their beloved ones, etc.). Therefore, an- of Reem Suwaileh was supported by GSRA grant#
alyzing the tweets to detect sentiment, stance, hate GSRA5-1-0527-18082 from the Qatar National Re-
speech, and generally, offensive language, among search Fund and the work of Fatima Haouari was
other aspects, is of interest to many stakeholders. supported by GSRA grant# GSRA6-1-0611-19074
89from the Qatar National Research Fund. The state- Yong Hu, He-Yan Huang, Anfan Chen, and Xian-Ling
ments made herein are solely the responsibility of Mao. 2020. Weibo-cov: A large-scale covid-19 so-
cial media dataset from weibo. In Proceedings of
the authors.
the 1st Workshop on NLP for COVID-19 (Part 2) at
EMNLP 2020.
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