CAREER: Social Response-Powered Misinformation Detection, Robustness, and Correction

This project will invent methods to detect and correct misinformation on online platforms. Online misinformation poses an alarming threat to public health, democracy, science, and society. Addressing misinformation at scale remains a pressing challenge as current solutions rely on the limited resources of professional fact-checkers or moderators, which neither scales to newly emerging information issues nor directly addresses how to respond to misinformation in situ. This project will address these challenges through developing robust detection models that leverage user-generated responses to social media posts to identify potentially non-credible information. The team will also design a counter-response generation tool that can help everyday users effectively respond to misinformation, leveraging the models developed along with existing fact-checking resources and best practices in communication to suggest possible responses to incorrect posts that will help readers assess them. Together, the proposed work will boost information literacy in society and reduce the number of people exposed to misinformation. The team will also develop interdisciplinary coursework and research opportunities that will broaden both students’ toolkits for addressing misinformation in social media systems and the range of students who engage in it. This project will advance scientific knowledge in misinformation, graph neural networks, adversarial learning, and social network analysis. The general approach is to leverage the social responses that ordinary users make on online posts, such as supporting, questioning, disbelieving, or countering claims, to robustly detect misinformation and suggest corrective responses. Around detection, the project will develop novel signed dynamic graph neural network models and network augmentation methods to address network sparsity issues. Around robustness, the project will create detection models that are robust to adversarial manipulation, by better modeling adversarial attacks carried out by groups of attackers, then creating defenses that optimize against fake response injections into social media comments. Around correction, the project seeks to empower social media users to correct misinformation by developing text generation methods to suggest effective counter-responses to posts estimated to contain information; these methods will be trained on data collected from professional fact-checking organizations and assessed in a series of studies. The project will also result in new models, datasets, benchmarks, and tools around misinformation that will promote future research on these topics. 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.