Crowd Intelligence for Early Misinformation Prediction on Social Media

• URL: http://arxiv.org/abs/2408.04463v1
• Date: Thu, 8 Aug 2024 13:45:23 GMT
• Title: Crowd Intelligence for Early Misinformation Prediction on Social Media
• Authors: Megha Sundriyal, Harshit Choudhary, Tanmoy Chakraborty, Md Shad Akhtar,
• Abstract summary: We introduce CROWDSHIELD, a crowd intelligence-based method for early misinformation prediction. We employ Q-learning to capture the two dimensions -- stances and claims. We propose MIST, a manually annotated misinformation detection Twitter corpus.
• Score: 29.494819549803772
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Misinformation spreads rapidly on social media, causing serious damage by influencing public opinion, promoting dangerous behavior, or eroding trust in reliable sources. It spreads too fast for traditional fact-checking, stressing the need for predictive methods. We introduce CROWDSHIELD, a crowd intelligence-based method for early misinformation prediction. We hypothesize that the crowd's reactions to misinformation reveal its accuracy. Furthermore, we hinge upon exaggerated assertions/claims and replies with particular positions/stances on the source post within a conversation thread. We employ Q-learning to capture the two dimensions -- stances and claims. We utilize deep Q-learning due to its proficiency in navigating complex decision spaces and effectively learning network properties. Additionally, we use a transformer-based encoder to develop a comprehensive understanding of both content and context. This multifaceted approach helps ensure the model pays attention to user interaction and stays anchored in the communication's content. We propose MIST, a manually annotated misinformation detection Twitter corpus comprising nearly 200 conversation threads with more than 14K replies. In experiments, CROWDSHIELD outperformed ten baseline systems, achieving an improvement of ~4% macro-F1 score. We conduct an ablation study and error analysis to validate our proposed model's performance. The source code and dataset are available at https://github.com/LCS2-IIITD/CrowdShield.git.

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