A r C orona: Analyzing A rabic Tweets in the Early Days of Coronavirus ( COVID -19) Pandemic (2021)

Abstract: Over the past few months, there were huge numbers of circulating tweets and discussions about Coronavirus (COVID-19) in the Arab region. It is important for policy makers and many people to identify types of shared tweets to better understand public behavior, topics of interest, requests from governments, sources of tweets, etc. It is also crucial to prevent spreading of rumors and misinformation about the virus or bad cures. To this end, we present the largest manually annotated dataset of Arabic tweets related to COVID-19. We describe annotation guidelines, analyze our dataset and build effective machine learning and transformer based models for classification.

## introduction as the coronavirus (covid-19) crippled lives across the world, people turned to social media to share their thoughts, news about vaccines or cures, personal stories, etc. with twitter being one of the popular social media platforms in the arab region, tweets became a major medium of discussion about covid-19. these tweets can be indicators of psychological and physical well being, public reactions to specific actions taken by the government and also public expectation from governments. therefore, identifying types of tweets and understanding their content can aid decision making by governments. it is also important for governments to identify and prevent rumours and bad cures since they can bring harm to society. while there have been many recent works about tweets related to covid-19, there are a very few targeted toward aiding governments in their decision making in the arab region despite arabic being one of the dominant languages on twitter @xcite . some of the existing works use automatically collected datasets @xcite . manually labeled datasets are either small in size (few hundred tweets) @xcite or target a different task such as sentiment analysis @xcite . to fill this gap, we present and publicly share the largest (to our best knowledge) manually annotated dataset of arabic tweets collected from early days of covid-19, labeled for 13 classes. we present our data collection and annotation scheme followed by data analysis, identifying trends, topics and distribution across countries. lastly, we employ machine learning and transformer models for classification. ## related work much of recent works on covid-19 rely on queries to twitter or distant supervision. this allows a large number of tweets to be collected. chen et al. (2020) collect 123m tweets by following certain queries and accounts on twitter. geocov19 @xcite ) is a large-scale dataset containing 524m tweets with their location information. banda et al. (2020) collected 152m tweets at the time of their writing. @xcite identifies situational information about covid-19 and its propagation on weibo. other works include propagation of misinformation @xcite , cultural, social and political impact of misinformation @xcite and rumor amplification @xcite . for arabic, we see a similar trend where few datasets are manually labeled. @xcite provide a large dataset of arabic tweets containing keywords related to covid-19. similarly, arcov-19 @xcite is a dataset of 750k tweets obtained by querying twitter. @xcite annotate a small number of english (currently 504) and arabic tweets (currently 218) for (i) existence of claim and worthiness of factchecking (ii) harmfulness to society, and (iii) relevance to governments or policy makers. yang et al. alsudias and rayson (2020) collected 1m unique arabic tweets related to covid-19 between december 2019 and april 2020. they used k-means algorithm from scikit-learn python package to cluster tweets into 5 clusters, namely: statistics, prayers, disease locations, advising, and advertising. they also annotated random 2000 tweets for rumor detection based on the tweets posted by the ministry of health in saudi arabia. ## data collection we used twarc search api foot_0 to collect tweets having the arabic word (corona) in feb and march 2020. we collected 30m tweets in total. the reason behind selecting this word is that it's widely used by normal people, news media foot_1 and official organizations 3 as opposed to 19-(covid-19) which is rarely used by normal people in different arab countries based on our observations. we aimed to increase diversity of tweet sources. our collection covers the period from feb 21 until march 31 in which coronavirus was reported for the first time in all arab countries except united arab emirates (ae) 4 (jan 29) and egypt (eg) (feb 14). the date of the first reported coronavirus case in lebanon (lb) was feb 21, and in iraq (iq), bahrain (bh), oman (om), and kuwait (kw) was feb 24, in qatar was feb 29, and in saudi arabia (sa) was march 2. all other arab countries came later. ## data annotation during the period of our study (40 days), we extracted the top retweeted 200 tweets in each day (total of 8000). we assume that the top retweeted tweets are the most important ones which get highest attention from twitter users. annotation was done manually by a native speaker who is familiar with arabic dialects according to class descriptions shown in note: if a tweet has multimedia (image or video) or an external link (url or another tweet), the annotator was asked to open it and judge accordingly to consider the full context of the tweet. for example, if a tweet has a text about a prayer and the attached image is about number of new cases, this should be classified as rep not prayer. data can be downloaded from this link foot_4 : https://alt.qcri.org/resources/arcorona. tsv . ## analysis class timeline is shown in figure 3 . we can observe the following important notes: we took a random sample of 1000 tweets and annotated them for their topics. figure 4 shows that, in addition to health, the virus affected many aspects of people's lives such as politics, economy, education, etc. we found also that 7% of tweets have hate speech, e.g. attacking china and iran for spreading the virus as shown in figure 5 . typically, people retweet tweets from ministry of health in their countries in addition to famous news agencies and celebrities. most of these accounts are verified. figure 4: tweet topics ## experiments and evaluation we randomly split the data into sets of 6000, 1000 and 1000 tweets for train, dev and test sets respectively. we report macro-averaged precision (p), recall (r) and f1 score along with accuracy (acc) on test set 6 . we use f1 score as primary metric for comparison. ## conclusion and future work we present the largest publicly available manually annotated dataset of arabic tweets for 13 classes that includes the most retweeted tweets in the early 8 https://huggingface.co/ figure 6 : confusion matrix normalized over true labels days of covid-19. followed by data analysis, we present models that can reliably identify important tweets and can perform fine-grained classification. in the future, we plan to compare our data to data from later days of the pandemic.