ACM Hypertext paper presentation

Abstract

The proliferation of social media has intensified the necessity for automated misinformation detection. Existing methods often strug- gle with early detection, as key information is not readily available during the initial dissemination stages. In this paper, we introduce a novel model for early misinformation detection on social media by classifying information propagation paths and leveraging linguistic patterns. Our model incorporates a causal user attribute inference model to label users as potential misinformation propagators or believers. Designed for early detection, the model includes two auxiliary tasks: forecasting the scope of misinformation dissemina- tion and clustering similar nodes (users) based on their attributes outperforming the current state-of-the-art benchmarks.

Shreya Ghosh

Postdoctoral Researcher (2021-2023)

My research interests include spatio-temporal data analytics and NLP (social media data analysis, low-resource language).