Fake News Detection Through Multi-Perspective Speaker Profiles

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

191 Citations (Scopus)

Abstract

Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.

Original languageEnglish
Title of host publicationProceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
EditorsGreg Kondrak, Taro Watanabe
PublisherAssociation for Computational Linguistics (ACL)
Pages252-256
Number of pages5
ISBN (Electronic)9781948087025
Publication statusPublished - 2017
Event8th International Joint Conference on Natural Language Processing, IJCNLP 2017 - Taipei, Taiwan
Duration: 27 Nov 20171 Dec 2017

Publication series

Name8th International Joint Conference on Natural Language Processing - Proceedings of the IJCNLP 2017, System Demonstrations
Volume2

Conference

Conference8th International Joint Conference on Natural Language Processing, IJCNLP 2017
Country/TerritoryTaiwan
CityTaipei
Period27/11/171/12/17

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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