Volume of content and misinformation on social media is rapidly increasing. There is a need for systems that can support fact checkers by prioritizing content that needs to be fact checked. Prior research on prioritizing content for fact-checking has focused on news media articles, predominantly in English language. Increasingly, misinformation is found in user-generated content. In this paper we present a novel dataset that can be used to prioritize check-worthy posts from multi-media content in Hindi. It is unique in its 1) focus on user generated content, 2) language and 3)  multi-modality  . In addition, we also provide metadata for each post such as number of shares and likes of the post on ShareChat, a popular Indian social media platform, that allows for correlative analysis around virality and misinformation  .
Volume of content and misinformation on social media is rapidly increasing. There is a need for systems that can support fact checkers by prioritizing content that needs to be fact checked. Prior research on prioritizing content for fact-checking has focused on news media articles, predominantly in English language. Increasingly, misinformation is found in user-generated content. In this paper we present a novel dataset that can be used to prioritize check-worthy posts from multi-media content in Hindi. It is unique in its 1) focus on user generated content, 2) language and 3) accommodation of multi-modality in social media posts . In addition, we also provide metadata for each post such as number of shares and likes of the post on ShareChat, a popular Indian social media platform, that allows for correlative analysis around virality and misinformation . The data is accessible on Zenodo URL under Creative Commons Attribution License (CC BY 4.0) .
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accommodation of
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in social media posts
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. The data is accessible on Zenodo URL under Creative Commons Attribution License (CC BY 4.0)