Crowd Intelligence for Early Misinformation Prediction on Social Media
<|reference_start|>Crowd Intelligence for Early Misinformation Prediction on Social Media: 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.<|reference_end|>
@article{sundriyal2024crowd,
    title={Crowd Intelligence for Early Misinformation Prediction on Social Media},
    author={Megha Sundriyal, Harshit Choudhary, Tanmoy Chakraborty, Md Shad Akhtar},
    journal={arXiv preprint arXiv:2408.04463},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04463},
    primaryClass={cs.CL}
}