  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.
