Same: Sentiment-aware multi-modal embedding for detecting fake news

Limeng Cui, Suhang Wang, Dongwon Lee

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

Abstract

How to effectively detect fake news and prevent its diffusion on social media has gained much attention in recent years. However, relatively little focus has been given on exploiting user comments left for posts and latent sentiments therein in detecting fake news. Inspired by the rich information available in user comments on social media, therefore, we investigate whether the latent sentiments hidden in user comments can potentially help distinguish fake news from reliable content. We incorporate users’ latent sentiments into an end-to-end deep embedding framework for detecting fake news, named as SAME. First, we use multi-modal networks to deal with heterogeneous data modalities. Second, to learn semantically meaningful spaces per data source, we adopt an adversarial mechanism. Third, we define a novel regularization loss to bring embeddings of relevant pairs closer. Our comprehensive validation using two real-world datasets, PolitiFact and GossipCop, demonstrates the effectiveness of SAME in detecting fake news, significantly outperforming state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
EditorsFrancesca Spezzano, Wei Chen, Xiaokui Xiao
PublisherAssociation for Computing Machinery, Inc
Pages41-48
Number of pages8
ISBN (Electronic)9781450368681
DOIs
StatePublished - Aug 27 2019
Event11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada
Duration: Aug 27 2019Aug 30 2019

Publication series

NameProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Conference

Conference11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
CountryCanada
CityVancouver
Period8/27/198/30/19

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All Science Journal Classification (ASJC) codes

  • Communication
  • Computer Networks and Communications
  • Information Systems and Management
  • Sociology and Political Science

Cite this

Cui, L., Wang, S., & Lee, D. (2019). Same: Sentiment-aware multi-modal embedding for detecting fake news. In F. Spezzano, W. Chen, & X. Xiao (Eds.), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 (pp. 41-48). (Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3341161.3342894