Frontiers in Big Data (May 2023)
Advances in AI for web integrity, equity, and well-being
Abstract
My research develops data mining, AI, and applied machine learning methods to combat malicious actors (sockpuppets, ban evaders, etc.) and dangerous content (misinformation, hate, etc.) on web platforms. My vision is to create a trustworthy online ecosystem for everyone and the next generation of socially-aware methods that promote health, equity, and integrity of users, communities, and platforms online. Broadly, in my research, I create novel graph, content (NLP, multimodality), and adversarial machine learning methods leveraging terabytes of data to detect, predict, and mitigate online threats. My interdisciplinary research innovates socio-technical solutions that I achieve by amalgamating computer science with social science theories. My research seeks to start a paradigm shift from the current slow and reactive approach against online harms to agile, proactive, and whole-of-society solutions. In this article, I shall describe my research efforts along four thrusts to achieve my goals: (1) Detection of harmful content and malicious actors across platforms, languages, and modalities; (2) Robust detection models against adversarial actors by predicting future malicious activities; (3) Attribution of the impact of harmful content in online and real world; and (4) Mitigation techniques to counter misinformation by professionals and non-expert crowds. Together, these thrusts give a set of holistic solutions to combat cyberharms. I am also passionate about putting my research into practice—my lab's models have been deployed on Flipkart, influenced Twitter's Birdwatch, and now being deployed on Wikipedia.
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