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Register a new userArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic
Arcorona: تحليل تغريدات عربية في الأيام الأولى من فيروس Coronavirus (Covid-19) جائحة
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خلال الأشهر القليلة الماضية، كانت هناك أعداد هائلة من التغريدات المتداولة والمناقشات حول Vironavirus (Covid-19) في المنطقة العربية.من المهم لصانعي السياسات والعديد من الأشخاص تحديد أنواع التغريدات المشتركة لفهم السلوك العام بشكل أفضل، ومواضيع المصالح، وطلبات الحكومات، ومصادر التغريدات، وما إلى ذلك. كما أنه من الأهمية بمكان انتشار شائعات وإضاءة في الفيروس أوعلاجات سيئة.تحقيقا لهذه الغاية، نقدم أكبر مجموعة بيانات مشروحة يدويا من تغريدات عربية تتعلق بالكوف (19).نحن تصف إرشادات التوضيحية، وتحليل DataSet لدينا وبناء نماذج التعلم والتحول في الآلات الفعالة للتصنيف.
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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In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pan
demic that includes about 2.7M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and -liked). The propagation networks include both retweetsand conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world.In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets.
Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spok
en in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution. We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction.
The spread of COVID-19 has been accompanied with widespread misinformation on social media. In particular, Twitterverse has seen a huge increase in dissemination of distorted facts and figures. The present work aims at identifying tweets regarding CO
VID-19 which contains harmful and false information. We have experimented with a number of Deep Learning-based models, including different word embeddings, such as Glove, ELMo, among others. BERTweet model achieved the best overall F1-score of 0.881 and secured the third rank on the above task.
While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news'' may hinder efforts to recover from the first one. With this in mind, we performed an extensive analysis o
f Arabic and English tweets about COVID-19 vaccines, with focus on messages originating from Qatar. We found that Arabic tweets contain a lot of false information and rumors, while English tweets are mostly factual. However, English tweets are much more propagandistic than Arabic ones. In terms of propaganda techniques, about half of the Arabic tweets express doubt, and 1/5 use loaded language, while English tweets are abundant in loaded language, exaggeration, fear, name-calling, doubt, and flag-waving. Finally, in terms of framing, Arabic tweets adopt a health and safety perspective, while in English economic concerns dominate.
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the othe
r task. In this paper, we additionally explore how much fine- tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).
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