III:Small: A novel machine learning framework for combating misinformation in real life

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Widespread misinformation has been persistently observed on social media, raising significant challenges to society. Misinformation involves rapidly evolving topics and sophisticated coordination among bad actors. Existing techniques to combat misinformation have achieved success to a limited extent. They struggle to eliminate false and misleading content in a timely and effective manner. Another challenge is the difficulty of quantifying the impacts of misinformation. In this project, a novel machine-learning framework will be developed by leveraging recent developments in machine learning. It will advance the understanding of misinformation propagation patterns, produce effective algorithms for misinformation detection, and help build a secure and trustworthy cyberspace. A range of outreach activities will be pursued to broaden participation in computing for women and other underrepresented groups. Tutorials and courses will be provided to broadcast the research outcomes.The project will advance both machine-learning methodologies and social science insights, leading to effective solutions to mitigate misinformation and manipulation in a timely, scalable, and effective manner.Three research thrusts will be pursued in this project. The first thrust develops a reinforcement-learning-based solution using news-source credibility analysis to minimize human labeling efforts in constructing large-scale misinformation datasets. The second thrust develops an unsupervised coordination detection method based on knowledge-informed machine-learning models to identify coordinated behaviors among bad actors. The third thrust develops novel counterfactual analysis models to identify causal factors and evaluate the estimated effects of misinformation. A collection of large-scale datasets of misinformation on a variety of topics will be shared with the research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.