Titlebook: Combating Online Hostile Posts in Regional Languages during Emergency Situation; First International Tanmoy Chakraborty,Kai Shu,Md Shad Ak

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g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detscussed on social media. However, not all social media posts are truthful. Many of them spread fake news that cause panic among readers, misinform people and thus exacerbate the effect of the pandemic. In this paper, we present our results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News D

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: ,arly Det,tion of ,VID ,ies Using Content, Prior Knowledge and Source Information,are domain. Therefore, it is important to automatically detect fake news in an early stage before they get widely spread. This paper analyzes the impact of incorporating content information, prior knowledge, and credibility of sources into models for the early detection of fake news. We propose a fr

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Identification of COVID-19 Related Fake News via Neural Stacking,olution to the shared task titled ., scoring the 50th place amongst 168 submissions. The solution was within 1.5% of the best performing solution. The proposed solution employs a heterogeneous representation ensemble, adapted for the classification task via an additional neural classification head c

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Fake News Detection System Using XLNet Model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Tout truthful information from fake ones. The research community is now faced with the task of automatic detection of fake news, which carries real-world socio-political impact. One such research contribution came in the form of the Constraint@AAA12021 Shared Task on COVID19 Fake News Detection in En

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