How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics

Shreya Ghosh, Prasenjit Mitra

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

Abstract

The rise of social media has amplified the need for automated detection of misinformation. Current methods face limitations in early detection because crucial information that they rely on is unavailable during the initial phases of information dissemination. This paper presents an innovative model for the early detection of misinformation on social media through the classification of information propagation paths and using linguistic patterns. We have developed and incorporated a causal user attribute inference model to label users as potential misinformation propagators or believers. Our model is designed for early detection of false information and includes two auxiliary tasks: predicting the extent of misinformation dissemination and clustering similar nodes (or users) based on their attributes. We demonstrate that our proposed model can identify fake news on real-world datasets with 86.5% accuracy within 30 min of its initial distribution and before it reaches 50 retweets, outperforming existing state-of-the-art benchmarks.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationApplied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings
EditorsGianmarco De Francisci Morales, Francesco Bonchi, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis, Elena Baralis
PublisherSpringer Science and Business Media Deutschland GmbH
Pages174-189
Number of pages16
ISBN (Print)9783031434266
DOIs
StatePublished - 2023
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy
Duration: Sep 18 2023Sep 22 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14174 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
Country/TerritoryItaly
CityTurin
Period9/18/239/22/23

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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