Catching Lies in the Act: A Framework for Early Misinformation Detection on Social Media

Shreya Ghosh, Prasenjit Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The proliferation of social media has intensified the necessity for automated misinformation detection. Existing methods often struggle 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 dissemination and clustering similar nodes (users) based on their attributes outperforming the current state-of-the-art benchmarks.

Original languageEnglish (US)
Title of host publicationHT 2023 - The 34th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400702327
DOIs
StatePublished - Sep 4 2023
Event34th ACM Conference on Hypertext and Social Media, HT 2023 - Rome, Italy
Duration: Sep 4 2023Sep 8 2023

Publication series

NameHT 2023 - The 34th ACM Conference on Hypertext and Social Media

Conference

Conference34th ACM Conference on Hypertext and Social Media, HT 2023
Country/TerritoryItaly
CityRome
Period9/4/239/8/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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