Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter

Masud, Sarah; Dutta, Subhabrata; Makkar, Sakshi; Jain, Chinmay; Goyal, Vikram; Das, Amitava; Chakraborty, Tanmoy

doi:10.48550/arxiv.2010.04377

Cited by 1 publication

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“…Although the character limit helps the amount of textual content being shared, other forms of content such as images, videos and links Although few users tweet and retweet false content without any ill-intention, there exist users who create and spread false news for political gains. Diffusion of fake tweets and genuine tweets vary in a pandemic setting such as this [12]. Tweeting a political tweet with false information multiple times from several accounts with various trending hashtags, called 'Hashtag hijacking' is also observed.…”

Section: Introductionmentioning

confidence: 96%

Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection

Paka¹,

Bansal²,

Kaushik³

et al. 2021

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As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts. During times such as the COVID-19 pandemic, fake news can not only cause intellectual confusion but can also place people's lives at risk. This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labelled genuine and fake tweets. Additionally, we propose Cross-SEAN, a crossstitch based semi-supervised end-to-end neural attention model, which leverages the large amount of unlabelled data. Cross-SEAN partially generalises to emerging fake news as it learns from relevant external knowledge. We compare Cross-SEAN with seven state-of-the-art fake news detection methods. We observe that it achieves 0.95 F1 Score on CTF, outperforming the best baseline by 9%. We also develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time detection of fake tweets.

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Section: Introductionmentioning

confidence: 96%