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"date_generated": "2023-01-19T12:10:28.730663Z" |
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"title": "ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic", |
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"authors": [ |
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{ |
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"first": "Hamdy", |
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"middle": [], |
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"last": "Mubarak", |
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"affiliation": { |
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"institution": "Qatar Computing Research Institute HBKU", |
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"location": { |
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"country": "Qatar" |
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}, |
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"email": "[email protected]" |
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{ |
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"first": "Sabit", |
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"middle": [], |
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"last": "Hassan", |
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"suffix": "", |
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"affiliation": { |
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"laboratory": "", |
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"institution": "Qatar Computing Research Institute HBKU", |
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"location": { |
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"country": "Qatar" |
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"email": "[email protected]" |
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"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.", |
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"text": "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.", |
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"text": "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.", |
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"section": "Introduction", |
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"text": "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 (Alshaabi et al., 2020) . Some of the existing works use automatically collected datasets (Alqurashi et al., 2020) . Manually labeled datasets are either small in size (few hundred tweets) (Alam et al., 2020) or target a different task such as sentiment analysis (Haouari et al., 2020) . 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.", |
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"text": "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 (Qazi et al., 2020 ) 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. Li et al. (2020) identifies situational information about COVID-19 and its propagation on Weibo. Other works include propagation of misinformation (Huang and Carley, 2020; Shahi et al., 2020) , cultural, social and political impact of misinformation (Leng et al., 2020) and rumor amplification (Cinelli et al., 2020) .", |
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"text": "For Arabic, we see a similar trend where few datasets are manually labeled. Alqurashi et al. (2020) provide a large dataset of Arabic tweets containing keywords related to COVID-19. Similarly, ArCOV-19 (Haouari et al., 2020 ) is a dataset of 750K tweets obtained by querying Twitter. Alam et al. (2020) 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. (2020) annotate 10K Arabic and English tweets for the task of fine-grained sentiment analysis.", |
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"text": "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.", |
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"text": "We used twarc search API 1 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 2 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.", |
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"text": "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 Table 1 . To measure quality, we annotated 200 random tweets by a second annotator. Inter-annotator agreement was 0.85 using Cohen's kappa coefficient which indicates high quality given that annotation is not trivial and some classes are close to each other. Examples of annotation classes are shown in Figures 1 and 2 .", |
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"text": "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 5 :", |
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"text": "https://alt.qcri.org/resources/ArCorona. tsv.", |
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"text": "We found that \u224810% of the tweets can take more than one class, e.g. a tweet reports new cases and a medical advice. We plan to allow multiple labels in future. In the current version, such tweets take the label of the first \"important\" class. We consider the first 8 classes in Table 1 to be important and the last 5 classes (PRSNL, SUPPORT, PRAYER, UNIMP and NOT ARB) to be less important. These classes will be merged into LessImportant class.", |
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"text": "Class timeline is shown in Figure 3 . We can observe the following important notes:", |
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"text": "\u2022 Large portion of tweets can be considered as LessImportant to many people (\u2248 30%). \u2022 Reports (REP) and actions taken by governments (ACT) are the most retweeted tweets. \u2022 Information about the virus (INFO) get less attention with time and there is an increasing number of tweets about volunteering (VOLUNT). \u2022 There are continuous requests for governments to take actions (SEEK ACT) -especially in the beginning (\u2248 15%), and few tweets are about rumors (\u2248 5%) and cures (\u2248 2%).", |
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"text": "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 . Table 2 shows country distribution and top accounts for the original authors of tweets.", |
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"text": "Typically, people retweet tweets from ministry of health in their countries in addition to famous news ", |
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"text": "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.", |
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"text": "We experimented with character and word n-gram features weighted by term 6 Differences between dev and test sets are \u00b12 \u2212 3% (F1). ", |
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"text": "Support Vector Machines (SVMs) SVMs have been shown to perform decently for Arabic text classification tasks such as spam detection , adult content detection , offensiveness detection (Hassan et al., 2020b,a) or dialect identification Bouamor et al., 2019) . We experimented with i) word n-gram, ii) character n-gram and iii) Mazajak Embeddings. We used LinearSVC implementation by scikit-learn 7 . Deep Contextualized Transformer Models (BERT) Transformer-based pre-trained contextual embeddings, such as BERT (Devlin et al., 2019) , have outperformed other classifiers in many NLP tasks. We used AraBERT (Antoun et al., 2020) , a BERT-based model trained on Arabic news. We used ktrain library (Maiya, 2020) that utilizes Huggingface 8 implementation to fine-tune AraBERT. We used learning rate of 8e -5 , truncating length of 50 and fine-tuned for 5 epochs.", |
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"text": "Bouamor et al., 2019)", |
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"text": "(Devlin et al., 2019)", |
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"text": "(Antoun et al., 2020)", |
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"text": "First, we experiment to distinguish LessImportant tweets from others (see Section 4). From Table 3 , we can see that SVMs with character [2-5]-gram outperformed others with F1 score of 79.8, closely followed by AraBERT with 79.6 F1.", |
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"section": "Binary Classification", |
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"text": "Our next set of experiments were designed for fine-grained classification for 13 classes. With F1 score of 60.5, AraBERT outperformed others (Table 4 ).", |
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"text": "(Table 4", |
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"text": "Error Analysis: AraBERT confusion matrix ( Figure 6 ) shows that PRSNL, INFO and RUMOR are the hardest classes to identify and the most common error is misclassifying INFO as ADVICE. We hypothesize these errors can be reduced if larger data set is being annotated.", |
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"text": "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.", |
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"section": "Conclusion and Future Work", |
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"text": "https://github.com/DocNow/twarc 2 https://www.aljazeera.com/topics/events/coronavirusoutbreak.html 3 https://www.who.int/ar/emergencies/diseases/novelcoronavirus-20194 We use ISO 3166-1 alpha-2 for country codes", |
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"text": "We share tweet id, date and class.", |
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"title": "This includes discussions and consequences of these measures. 3. INFO Information about the virus, symptoms, incubation period, how it spreads, mask types, etc. 300 4. RUMOR Rumor or refute rumor. A rumor is a circulating story or report of uncertain or doubtful truth. 421 5. ADVICE Advice such as washing hands", |
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"venue": "and announcements such as number of infections, recovery cases and deaths. 1664 2", |
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"raw_text": "and announcements such as number of infections, recovery cases and deaths. 1664 2. ACT Measures or actions taken by governments such as curfew, closing of country borders, 1383 shops and worship places. This includes discussions and consequences of these measures. 3. INFO Information about the virus, symptoms, incubation period, how it spreads, mask types, etc. 300 4. RUMOR Rumor or refute rumor. A rumor is a circulating story or report of uncertain or doubtful truth. 421 5. ADVICE Advice such as washing hands, staying at home, wearing masks and avoiding travel. 1047", |
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"title": "CURE News about good and bad cure, diagnosis, ventilators, supportive medical equipment, etc. 116 8. VOLUNT Volunteering efforts or donation of money", |
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"venue": "SEEK ACT Seek actions from governments such as closing airports, and controlling prices of goods", |
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"raw_text": "SEEK ACT Seek actions from governments such as closing airports, and controlling prices of goods. 587 7. CURE News about good and bad cure, diagnosis, ventilators, supportive medical equipment, etc. 116 8. VOLUNT Volunteering efforts or donation of money, goods or services.", |
|
"links": null |
|
}, |
|
"BIBREF2": { |
|
"ref_id": "b2", |
|
"title": "Arabic dialect identification in the wild", |
|
"authors": [ |
|
{ |
|
"first": "Hamdy", |
|
"middle": [], |
|
"last": "References Ahmed Abdelali", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Younes", |
|
"middle": [], |
|
"last": "Mubarak", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Sabit", |
|
"middle": [], |
|
"last": "Samih", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kareem", |
|
"middle": [], |
|
"last": "Hassan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "", |
|
"middle": [], |
|
"last": "Darwish", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "ArXiv", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "References Ahmed Abdelali, Hamdy Mubarak, Younes Samih, Sabit Hassan, and Kareem Darwish. 2020. Ara- bic dialect identification in the wild. ArXiv, abs/2005.06557.", |
|
"links": null |
|
}, |
|
"BIBREF3": { |
|
"ref_id": "b3", |
|
"title": "Mazajak: An online Arabic sentiment analyser", |
|
"authors": [ |
|
{ |
|
"first": "Ibrahim", |
|
"middle": [], |
|
"last": "Abu Farha", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Walid", |
|
"middle": [], |
|
"last": "Magdy", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2019, |
|
"venue": "Proceedings of the Fourth Arabic Natural Language Processing Workshop", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "192--198", |
|
"other_ids": { |
|
"DOI": [ |
|
"10.18653/v1/W19-4621" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Ibrahim Abu Farha and Walid Magdy. 2019. Mazajak: An online Arabic sentiment analyser. In Proceed- ings of the Fourth Arabic Natural Language Process- ing Workshop, pages 192-198, Florence, Italy. Asso- ciation for Computational Linguistics.", |
|
"links": null |
|
}, |
|
"BIBREF4": { |
|
"ref_id": "b4", |
|
"title": "Fighting the covid-19 infodemic: Modeling the perspective of journalists, fact-checkers, social media platforms, policy makers, and the society", |
|
"authors": [ |
|
{ |
|
"first": "Firoj", |
|
"middle": [], |
|
"last": "Alam", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Shaden", |
|
"middle": [], |
|
"last": "Shaar", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Fahim", |
|
"middle": [], |
|
"last": "Dalvi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Hassan", |
|
"middle": [], |
|
"last": "Sajjad", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Alex", |
|
"middle": [], |
|
"last": "Nikolov", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Hamdy", |
|
"middle": [], |
|
"last": "Mubarak", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Giovanni", |
|
"middle": [], |
|
"last": "Da San", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ahmed", |
|
"middle": [], |
|
"last": "Martino", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Nadir", |
|
"middle": [], |
|
"last": "Abdelali", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kareem", |
|
"middle": [], |
|
"last": "Durrani", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Preslav", |
|
"middle": [], |
|
"last": "Darwish", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "", |
|
"middle": [], |
|
"last": "Nakov", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2005.00033" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, and Preslav Nakov. 2020. Fighting the covid-19 infodemic: Modeling the perspective of journalists, fact-checkers, social media platforms, policy makers, and the society. arXiv:2005.00033.", |
|
"links": null |
|
}, |
|
"BIBREF5": { |
|
"ref_id": "b5", |
|
"title": "Large arabic twitter dataset on covid-19", |
|
"authors": [ |
|
{ |
|
"first": "Sarah", |
|
"middle": [], |
|
"last": "Alqurashi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ahmad", |
|
"middle": [], |
|
"last": "Alhindi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Eisa", |
|
"middle": [], |
|
"last": "Alanazi", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2004.04315" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Sarah Alqurashi, Ahmad Alhindi, and Eisa Alanazi. 2020. Large arabic twitter dataset on covid-19. arXiv preprint arXiv:2004.04315.", |
|
"links": null |
|
}, |
|
"BIBREF7": { |
|
"ref_id": "b7", |
|
"title": "The growing amplification of social media: Measuring temporal and social contagion dynamics for over 150 languages on twitter for 2009-2020", |
|
"authors": [ |
|
{ |
|
"first": "Peter", |
|
"middle": [ |
|
"Sheridan" |
|
], |
|
"last": "Danforth", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "", |
|
"middle": [], |
|
"last": "Dodds", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Danforth, and Peter Sheridan Dodds. 2020. The growing amplification of social media: Measuring temporal and social contagion dynamics for over 150 languages on twitter for 2009-2020. Available online at http://arxiv.org/abs/2003.03667.", |
|
"links": null |
|
}, |
|
"BIBREF8": { |
|
"ref_id": "b8", |
|
"title": "COVID-19 and Arabic Twitter: How can Arab world governments and public health organizations learn from social media?", |
|
"authors": [ |
|
{ |
|
"first": "Lama", |
|
"middle": [], |
|
"last": "Alsudias", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Paul", |
|
"middle": [], |
|
"last": "Rayson", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Lama Alsudias and Paul Rayson. 2020. COVID-19 and Arabic Twitter: How can Arab world governments and public health organizations learn from social me- dia? In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.", |
|
"links": null |
|
}, |
|
"BIBREF9": { |
|
"ref_id": "b9", |
|
"title": "Arabert: Transformer-based model for arabic language understanding", |
|
"authors": [ |
|
{ |
|
"first": "Wissam", |
|
"middle": [], |
|
"last": "Antoun", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Fady", |
|
"middle": [], |
|
"last": "Baly", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Hazem", |
|
"middle": [ |
|
"M" |
|
], |
|
"last": "Hajj", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "ArXiv", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Wissam Antoun, Fady Baly, and Hazem M. Hajj. 2020. Arabert: Transformer-based model for arabic lan- guage understanding. ArXiv, abs/2003.00104.", |
|
"links": null |
|
}, |
|
"BIBREF10": { |
|
"ref_id": "b10", |
|
"title": "Yuning Ding, and Gerardo Chowell. 2020. A large-scale covid-19 twitter chatter dataset for open scientific researchan international collaboration", |
|
"authors": [ |
|
{ |
|
"first": "M", |
|
"middle": [], |
|
"last": "Juan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ramya", |
|
"middle": [], |
|
"last": "Banda", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Guanyu", |
|
"middle": [], |
|
"last": "Tekumalla", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Jingyuan", |
|
"middle": [], |
|
"last": "Wang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Tuo", |
|
"middle": [], |
|
"last": "Yu", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "", |
|
"middle": [], |
|
"last": "Liu", |
|
"suffix": "" |
|
} |
|
], |
|
"year": null, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2004.03688" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Juan M Banda, Ramya Tekumalla, Guanyu Wang, Jingyuan Yu, Tuo Liu, Yuning Ding, and Ger- ardo Chowell. 2020. A large-scale covid-19 twit- ter chatter dataset for open scientific research- an international collaboration. arXiv preprint arXiv:2004.03688.", |
|
"links": null |
|
}, |
|
"BIBREF11": { |
|
"ref_id": "b11", |
|
"title": "The MADAR shared task on Arabic finegrained dialect identification", |
|
"authors": [ |
|
{ |
|
"first": "Houda", |
|
"middle": [], |
|
"last": "Bouamor", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Sabit", |
|
"middle": [], |
|
"last": "Hassan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Nizar", |
|
"middle": [], |
|
"last": "Habash", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2019, |
|
"venue": "Proceedings of the Fourth Arabic Natural Language Processing Workshop", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "199--207", |
|
"other_ids": { |
|
"DOI": [ |
|
"10.18653/v1/W19-4622" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Houda Bouamor, Sabit Hassan, and Nizar Habash. 2019. The MADAR shared task on Arabic fine- grained dialect identification. In Proceedings of the Fourth Arabic Natural Language Processing Work- shop, pages 199-207, Florence, Italy. Association for Computational Linguistics.", |
|
"links": null |
|
}, |
|
"BIBREF12": { |
|
"ref_id": "b12", |
|
"title": "Tracking social media discourse about the covid-19 pandemic: Development of a public coronavirus twitter data set", |
|
"authors": [ |
|
{ |
|
"first": "Emily", |
|
"middle": [], |
|
"last": "Chen", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kristina", |
|
"middle": [], |
|
"last": "Lerman", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Emilio", |
|
"middle": [], |
|
"last": "Ferrara", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "JMIR Public Health Surveill", |
|
"volume": "6", |
|
"issue": "2", |
|
"pages": "", |
|
"other_ids": { |
|
"DOI": [ |
|
"10.2196/19273" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Emily Chen, Kristina Lerman, and Emilio Ferrara. 2020. Tracking social media discourse about the covid-19 pandemic: Development of a public coron- avirus twitter data set. JMIR Public Health Surveill, 6(2):e19273.", |
|
"links": null |
|
}, |
|
"BIBREF13": { |
|
"ref_id": "b13", |
|
"title": "The covid-19 social media infodemic", |
|
"authors": [ |
|
{ |
|
"first": "Matteo", |
|
"middle": [], |
|
"last": "Cinelli", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Walter", |
|
"middle": [], |
|
"last": "Quattrociocchi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Alessandro", |
|
"middle": [], |
|
"last": "Galeazzi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Carlo", |
|
"middle": [ |
|
"Michele" |
|
], |
|
"last": "Valensise", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Emanuele", |
|
"middle": [], |
|
"last": "Brugnoli", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ana", |
|
"middle": [ |
|
"Lucia" |
|
], |
|
"last": "Schmidt", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Paola", |
|
"middle": [], |
|
"last": "Zola", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Fabiana", |
|
"middle": [], |
|
"last": "Zollo", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Antonio", |
|
"middle": [], |
|
"last": "Scala", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2003.05004" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Matteo Cinelli, Walter Quattrociocchi, Alessandro Galeazzi, Carlo Michele Valensise, Emanuele Brug- noli, Ana Lucia Schmidt, Paola Zola, Fabiana Zollo, and Antonio Scala. 2020. The covid-19 social media infodemic. arXiv preprint arXiv:2003.05004.", |
|
"links": null |
|
}, |
|
"BIBREF14": { |
|
"ref_id": "b14", |
|
"title": "BERT: Pre-training of deep bidirectional transformers for language understanding", |
|
"authors": [ |
|
{ |
|
"first": "Jacob", |
|
"middle": [], |
|
"last": "Devlin", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ming-Wei", |
|
"middle": [], |
|
"last": "Chang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kenton", |
|
"middle": [], |
|
"last": "Lee", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kristina", |
|
"middle": [], |
|
"last": "Toutanova", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2019, |
|
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
|
"volume": "1", |
|
"issue": "", |
|
"pages": "4171--4186", |
|
"other_ids": { |
|
"DOI": [ |
|
"10.18653/v1/N19-1423" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.", |
|
"links": null |
|
}, |
|
"BIBREF15": { |
|
"ref_id": "b15", |
|
"title": "Reem Suwaileh, and Tamer Elsayed. 2020. ArCOV-19: The first Arabic COVID-19 twitter dataset with propagation networks", |
|
"authors": [ |
|
{ |
|
"first": "Fatima", |
|
"middle": [], |
|
"last": "Haouari", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Maram", |
|
"middle": [], |
|
"last": "Hasanain", |
|
"suffix": "" |
|
} |
|
], |
|
"year": null, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2004.05861" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Fatima Haouari, Maram Hasanain, Reem Suwaileh, and Tamer Elsayed. 2020. ArCOV-19: The first Ara- bic COVID-19 twitter dataset with propagation net- works. arXiv:2004.05861.", |
|
"links": null |
|
}, |
|
"BIBREF16": { |
|
"ref_id": "b16", |
|
"title": "Asad: Arabic social media analytics and understanding", |
|
"authors": [ |
|
{ |
|
"first": "Sabit", |
|
"middle": [], |
|
"last": "Hassan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Hamdy", |
|
"middle": [], |
|
"last": "Mubarak", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ahmed", |
|
"middle": [], |
|
"last": "Abdelali", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kareem", |
|
"middle": [], |
|
"last": "Darwish", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2021, |
|
"venue": "Proceedings of the Software Demonstrations of the 16th Conference of the European Chapter of the Association for Computational Linguistics", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Sabit Hassan, Hamdy Mubarak, Ahmed Abdelali, and Kareem Darwish. 2021. Asad: Arabic social media analytics and understanding. In Proceedings of the Software Demonstrations of the 16th Conference of the European Chapter of the Association for Compu- tational Linguistics.", |
|
"links": null |
|
}, |
|
"BIBREF17": { |
|
"ref_id": "b17", |
|
"title": "ALT at SemEval-2020 task 12: Arabic and English offensive language identification in social media", |
|
"authors": [ |
|
{ |
|
"first": "Sabit", |
|
"middle": [], |
|
"last": "Hassan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Younes", |
|
"middle": [], |
|
"last": "Samih", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Hamdy", |
|
"middle": [], |
|
"last": "Mubarak", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ahmed", |
|
"middle": [], |
|
"last": "Abdelali", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "Proceedings of the Fourteenth Workshop on Semantic Evaluation", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "1891--1897", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Sabit Hassan, Younes Samih, Hamdy Mubarak, and Ahmed Abdelali. 2020a. ALT at SemEval-2020 task 12: Arabic and English offensive language iden- tification in social media. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1891-1897, Barcelona (online). International Com- mittee for Computational Linguistics.", |
|
"links": null |
|
}, |
|
"BIBREF18": { |
|
"ref_id": "b18", |
|
"title": "Ammar Rashed, and Shammur Absar Chowdhury. 2020b. ALT submission for OSACT shared task on offensive language detection", |
|
"authors": [ |
|
{ |
|
"first": "Sabit", |
|
"middle": [], |
|
"last": "Hassan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Younes", |
|
"middle": [], |
|
"last": "Samih", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Hamdy", |
|
"middle": [], |
|
"last": "Mubarak", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ahmed", |
|
"middle": [], |
|
"last": "Abdelali", |
|
"suffix": "" |
|
} |
|
], |
|
"year": null, |
|
"venue": "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "61--65", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Sabit Hassan, Younes Samih, Hamdy Mubarak, Ahmed Abdelali, Ammar Rashed, and Shammur Absar Chowdhury. 2020b. ALT submission for OSACT shared task on offensive language detection. In Pro- ceedings of the 4th Workshop on Open-Source Ara- bic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 61-65,", |
|
"links": null |
|
}, |
|
"BIBREF19": { |
|
"ref_id": "b19", |
|
"title": "European Language Resource Association", |
|
"authors": [ |
|
{ |
|
"first": "France", |
|
"middle": [], |
|
"last": "Marseille", |
|
"suffix": "" |
|
} |
|
], |
|
"year": null, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Marseille, France. European Language Resource As- sociation.", |
|
"links": null |
|
}, |
|
"BIBREF20": { |
|
"ref_id": "b20", |
|
"title": "Disinformation and misinformation on twitter during the novel coronavirus outbreak", |
|
"authors": [ |
|
{ |
|
"first": "Binxuan", |
|
"middle": [], |
|
"last": "Huang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kathleen", |
|
"middle": [ |
|
"M" |
|
], |
|
"last": "Carley", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2006.04278" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Binxuan Huang and Kathleen M Carley. 2020. Dis- information and misinformation on twitter during the novel coronavirus outbreak. arXiv preprint arXiv:2006.04278.", |
|
"links": null |
|
}, |
|
"BIBREF21": { |
|
"ref_id": "b21", |
|
"title": "Analysis of misinformation during the covid-19 outbreak in china: cultural, social and political entanglements", |
|
"authors": [ |
|
{ |
|
"first": "Yan", |
|
"middle": [], |
|
"last": "Leng", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Yujia", |
|
"middle": [], |
|
"last": "Zhai", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Shaojing", |
|
"middle": [], |
|
"last": "Sun", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Yifei", |
|
"middle": [], |
|
"last": "Wu", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Jordan", |
|
"middle": [], |
|
"last": "Selzer", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Sharon", |
|
"middle": [], |
|
"last": "Strover", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Julia", |
|
"middle": [], |
|
"last": "Fensel", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2005.10414" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Yan Leng, Yujia Zhai, Shaojing Sun, Yifei Wu, Jor- dan Selzer, Sharon Strover, Julia Fensel, Alex Pent- land, and Ying Ding. 2020. Analysis of misinforma- tion during the covid-19 outbreak in china: cultural, social and political entanglements. arXiv preprint arXiv:2005.10414.", |
|
"links": null |
|
}, |
|
"BIBREF22": { |
|
"ref_id": "b22", |
|
"title": "Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo", |
|
"authors": [ |
|
{ |
|
"first": "L", |
|
"middle": [], |
|
"last": "Li", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Q", |
|
"middle": [], |
|
"last": "Zhang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "X", |
|
"middle": [], |
|
"last": "Wang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "J", |
|
"middle": [], |
|
"last": "Zhang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "T", |
|
"middle": [], |
|
"last": "Wang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "T", |
|
"middle": [], |
|
"last": "Gao", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "W", |
|
"middle": [], |
|
"last": "Duan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "K", |
|
"middle": [ |
|
"K" |
|
], |
|
"last": "Tsoi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "F", |
|
"middle": [], |
|
"last": "Wang", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "IEEE Transactions on Computational Social Systems", |
|
"volume": "7", |
|
"issue": "2", |
|
"pages": "556--562", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "L. Li, Q. Zhang, X. Wang, J. Zhang, T. Wang, T. Gao, W. Duan, K. K. Tsoi, and F. Wang. 2020. Charac- terizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computa- tional Social Systems, 7(2):556-562.", |
|
"links": null |
|
}, |
|
"BIBREF23": { |
|
"ref_id": "b23", |
|
"title": "ktrain: A low-code library for augmented machine learning", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [], |
|
"last": "Arun", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "", |
|
"middle": [], |
|
"last": "Maiya", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2004.10703" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Arun S Maiya. 2020. ktrain: A low-code library for augmented machine learning. arXiv preprint arXiv:2004.10703.", |
|
"links": null |
|
}, |
|
"BIBREF24": { |
|
"ref_id": "b24", |
|
"title": "Spam detection on arabic twitter", |
|
"authors": [ |
|
{ |
|
"first": "Hamdy", |
|
"middle": [], |
|
"last": "Mubarak", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ahmed", |
|
"middle": [], |
|
"last": "Abdelali", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Sabit", |
|
"middle": [], |
|
"last": "Hassan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Kareem", |
|
"middle": [], |
|
"last": "Darwish", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "Social Informatics", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "237--251", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Hamdy Mubarak, Ahmed Abdelali, Sabit Hassan, and Kareem Darwish. 2020. Spam detection on ara- bic twitter. In Social Informatics, pages 237-251, Cham. Springer International Publishing.", |
|
"links": null |
|
}, |
|
"BIBREF25": { |
|
"ref_id": "b25", |
|
"title": "Adult content detection on arabic twitter: Analysis and experiments", |
|
"authors": [ |
|
{ |
|
"first": "Hamdy", |
|
"middle": [], |
|
"last": "Mubarak", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Sabit", |
|
"middle": [], |
|
"last": "Hassan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Ahmed", |
|
"middle": [], |
|
"last": "Abdelali", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2021, |
|
"venue": "Proceedings of the Sixth Arabic Natural Language Processing Workshop", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Hamdy Mubarak, Sabit Hassan, and Ahmed Abdelali. 2021. Adult content detection on arabic twitter: Analysis and experiments. In Proceedings of the Sixth Arabic Natural Language Processing Work- shop.", |
|
"links": null |
|
}, |
|
"BIBREF27": { |
|
"ref_id": "b27", |
|
"title": "An exploratory study of covid-19 misinformation on twitter", |
|
"authors": [ |
|
{ |
|
"first": "Anne", |
|
"middle": [], |
|
"last": "Gautam Kishore Shahi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Tim", |
|
"middle": [ |
|
"A" |
|
], |
|
"last": "Dirkson", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "", |
|
"middle": [], |
|
"last": "Majchrzak", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2005.05710" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Gautam Kishore Shahi, Anne Dirkson, and Tim A Majchrzak. 2020. An exploratory study of covid- 19 misinformation on twitter. arXiv preprint arXiv:2005.05710.", |
|
"links": null |
|
}, |
|
"BIBREF28": { |
|
"ref_id": "b28", |
|
"title": "Senwave: Monitoring the global sentiments under the covid-19 pandemic", |
|
"authors": [ |
|
{ |
|
"first": "Qiang", |
|
"middle": [], |
|
"last": "Yang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Hind", |
|
"middle": [], |
|
"last": "Alamro", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Somayah", |
|
"middle": [], |
|
"last": "Albaradei", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Adil", |
|
"middle": [], |
|
"last": "Salhi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Xiaoting", |
|
"middle": [], |
|
"last": "Lv", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Changsheng", |
|
"middle": [], |
|
"last": "Ma", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Manal", |
|
"middle": [], |
|
"last": "Alshehri", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Inji", |
|
"middle": [], |
|
"last": "Jaber", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Faroug", |
|
"middle": [], |
|
"last": "Tifratene", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Wei", |
|
"middle": [], |
|
"last": "Wang", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2020, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": { |
|
"arXiv": [ |
|
"arXiv:2006.10842" |
|
] |
|
}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Qiang Yang, Hind Alamro, Somayah Albaradei, Adil Salhi, Xiaoting Lv, Changsheng Ma, Manal Al- shehri, Inji Jaber, Faroug Tifratene, Wei Wang, et al. 2020. Senwave: Monitoring the global senti- ments under the covid-19 pandemic. arXiv preprint arXiv:2006.10842.", |
|
"links": null |
|
} |
|
}, |
|
"ref_entries": { |
|
"FIGREF0": { |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null, |
|
"text": "Examples for CURE, VOLUNT, PRSNL, SUPPORT, PRAYER and UNIMP classes agencies and celebrities. Most of these accounts are verified." |
|
}, |
|
"FIGREF1": { |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null, |
|
"text": "Timeline: Number of class tweets each day" |
|
}, |
|
"FIGREF2": { |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null, |
|
"text": "Figure 4: Tweet topics" |
|
}, |
|
"TABREF1": { |
|
"html": null, |
|
"type_str": "table", |
|
"num": null, |
|
"content": "<table><tr><td>: Annotation classes and distribution: Important classes (top) and LessImportant classes (bottom)</td></tr></table>", |
|
"text": "" |
|
}, |
|
"TABREF3": { |
|
"html": null, |
|
"type_str": "table", |
|
"num": null, |
|
"content": "<table><tr><td>: Country distribution and top accounts</td></tr><tr><td>frequency-inverse term document frequency (tf-</td></tr><tr><td>idf). We report results for only the most significant</td></tr><tr><td>ranges, namely, word [1-2] and character [2-5].</td></tr><tr><td>Mazajak Embeddings Mazajak embeddings are</td></tr><tr><td>word-level skip-gram embeddings trained on 250M</td></tr><tr><td>Arabic tweets, yielding 300-dimensional vectors</td></tr><tr><td>(Abu Farha and Magdy, 2019).</td></tr></table>", |
|
"text": "" |
|
}, |
|
"TABREF5": { |
|
"html": null, |
|
"type_str": "table", |
|
"num": null, |
|
"content": "<table><tr><td>Model</td><td colspan=\"2\">Features Acc.</td><td>P</td><td>R</td><td>F1</td></tr><tr><td>Majority Class</td><td>-</td><td>22.7</td><td>1.7</td><td>7.7</td><td>2.8</td></tr><tr><td>SVM</td><td>W[1-2]</td><td colspan=\"4\">62.8 64.3 53.5 56.3</td></tr><tr><td>SVM</td><td>C[2-5]</td><td colspan=\"4\">59.0 64.3 49.4 51.8</td></tr><tr><td>SVM</td><td>Mazajak</td><td colspan=\"4\">60.0 55.1 51.5 52.4</td></tr><tr><td>AraBERT</td><td/><td colspan=\"4\">62.7 61.6 59.8 60.5</td></tr></table>", |
|
"text": "Binary classification results" |
|
}, |
|
"TABREF6": { |
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"html": null, |
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"num": null, |
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"content": "<table/>", |
|
"text": "Fine-grained classification results" |
|
} |
|
} |
|
} |
|
} |