Check Mate: Prioritizing User Generated Multi-Media Content for Fact-Checking

TL;DR

This paper introduces a new dataset for prioritizing user-generated multi-media social media posts in Hindi for fact-checking, addressing the gap in multilingual and multi-modal misinformation detection.

Contribution

It presents a novel dataset focusing on Hindi user-generated multi-media content, including metadata, to aid in prioritizing posts for fact-checking, expanding research beyond English news articles.

Findings

Dataset includes multi-modal social media posts with metadata

Enables analysis of virality and misinformation correlation

Supports development of fact-checking prioritization tools

Abstract

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 (https://zenodo.org/record/4032629) under Creative Commons Attribution License (CC BY 4.0).