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Register a new userFighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
مكافحة المعكرات المعاكسة 19 المعكرية: النمذجة منظور الصحفيين، ودخرون الحقائق، ومنصات وسائل التواصل الاجتماعي، وصانعي السياسات، والمجتمع
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مع ظهور جائحة Covid-19، تم دمج الجوانب السياسية والطبية المتمثلة في التضليل حيث حصلت المشكلة على ارتفاع إلى مستوى جديد تماما لتصبح أول انفجار عالمي. تم إعلان محاربة هذا المعف النقود أحد أهم مجالات التركيز في منظمة الصحة العالمية، مع وجود مخاطر تتراوح من تعزيز العلاجات المزيفة والشائعات ونظريات المؤامرة لنشر كراهية الأجانب والذعر. يتطلب معالجة القضية حل عدد من المشاكل الصعبة مثل تحديد الرسائل التي تحتوي على مطالبات، وتحديد الجدارة الشيكية والوصيل لها، وإمكاناتها لإيذاءها وكذلك طبيعة هذا الضرر، لنذكر عدد قليل فقط. لمعالجة هذه الفجوة، نقوم بإصدار مجموعة بيانات كبيرة من 16 ألف تغريدات مشروح يدويا لتحليل التهيئة الدقيقة التي يركز عليها (ط) على CovID-19، (II) تجمع بين وجهات النظر ومصالح الصحفيين، ومساجي الحقائق، ومنصات وسائل التواصل الاجتماعي ، صانعي السياسات، والمجتمع، و (3) يغطي العربية والكبلانية والهولندية والإنجليزية. أخيرا، نظهر نتائج تقييم قوية باستخدام محولات مسبقا، وبالتالي تؤكد الأداة المساعدة العملة لمجموعة البيانات في أحادي التوتر مقابل تعدد اللغات، ومهمة واحدة مقابل إعدادات متعددة.
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.
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The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. In recent years, supervis
ed machine learning models have been used to automatically identify false information in social media. However, most of these machine learning models focus only on the language they were trained on. Given the fact that social media platforms are being used in different languages, managing machine learning models for each and every language separately would be chaotic. In this research, we experiment with multilingual models to identify false information in social media by using two recently released multilingual false information detection datasets. We show that multilingual models perform on par with the monolingual models and sometimes even better than the monolingual models to detect false information in social media making them more useful in real-world scenarios.
This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-
the-art contextualized text representation models that were fine-tuned for the downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set.
This paper describes the winning model in the Arabic NLP4IF shared task for fighting the COVID-19 infodemic. The goal of the shared task is to check disinformation about COVID-19 in Arabic tweets. Our proposed model has been ranked 1st with an F1-Sco
re of 0.780 and an Accuracy score of 0.762. A variety of transformer-based pre-trained language models have been experimented with through this study. The best-scored model is an ensemble of AraBERT-Base, Asafya-BERT, and ARBERT models. One of the study's key findings is showing the effect the pre-processing can have on every model's score. In addition to describing the winning model, the current study shows the error analysis.
We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that twe
et contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leaderboards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021.
The objective of this work was the introduction of an effective approach based on the AraBERT language model for fighting Tweets COVID-19 Infodemic. It was arranged in the form of a two-step pipeline, where the first step involved a series of pre-pro
cessing procedures to transform Twitter jargon, including emojis and emoticons, into plain text, and the second step exploited a version of AraBERT, which was pre-trained on plain text, to fine-tune and classify the tweets with respect to their Label. The use of language models pre-trained on plain texts rather than on tweets was motivated by the necessity to address two critical issues shown by the scientific literature, namely (1) pre-trained language models are widely available in many languages, avoiding the time-consuming and resource-intensive model training directly on tweets from scratch, allowing to focus only on their fine-tuning; (2) available plain text corpora are larger than tweet-only ones, allowing for better performance.
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