Multi-emotion Recognition Using Multi-EmoBERT and Emotion Analysis in Fake News

Jinfen Li, Lu Xiao

Research output: Chapter in Book/Entry/PoemConference contribution

4 Scopus citations


Emotion recognition techniques are increasingly applied in fake news veracity or stance detection. While multiple co-existing emotions tend to co-occur in a single news article, most existing fake news detection has only leveraged single-label emotion recognition mechanisms. In addition, the relationship between the emotion of an article and its stance has not been sufficiently explored. To address these research gaps, we have developed and applied a multi-label emotion recognition tool called Multi-EmoBERT in fake news datasets. The tool delivers state-of-the-art performance on SemEval2018 Task 1. We apply the tool to identify emotions in several fake news datasets and examine the relationships between veracity/stance and emotion. Our work demonstrates the potential for predicting multiple co-existing emotions for fake news and implications against fake news spread.

Original languageEnglish (US)
Title of host publicationWebSci 2023 - Proceedings of the 15th ACM Web Science Conference
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9798400700897
StatePublished - Apr 30 2023
Event15th ACM Web Science Conference, WebSci 2023 - Austin, United States
Duration: Apr 30 2023May 1 2023

Publication series

NameACM International Conference Proceeding Series


Conference15th ACM Web Science Conference, WebSci 2023
Country/TerritoryUnited States


  • emotion analysis
  • fake news detection
  • multi-emotion recognition

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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