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

Published: 30 April 2023 Publication History

Abstract

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.

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cover image ACM Conferences
WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023
April 2023
373 pages
ISBN:9798400700897
DOI:10.1145/3578503
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 30 April 2023

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Author Tags

  1. emotion analysis
  2. fake news detection
  3. multi-emotion recognition

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WebSci '23
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WebSci '23: 15th ACM Web Science Conference 2023
April 30 - May 1, 2023
TX, Austin, USA

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Overall Acceptance Rate 245 of 933 submissions, 26%

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  • (2024)The Sentiment of Fake News2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN61752.2024.10625004(324-329)Online publication date: 2-Jul-2024
  • (2024)Emotion detection for misinformationInformation Fusion10.1016/j.inffus.2024.102300107:COnline publication date: 2-Jul-2024
  • (2024)EmoBART: Multi-label Emotion Classification Method Based on Pre-trained Label Sequence Generation ModelNeural Computing for Advanced Applications10.1007/978-981-97-7007-6_8(104-115)Online publication date: 22-Sep-2024
  • (2024)Toward Detection of Fake News Using Sentiment Analysis for Albanian News ArticlesAdvances in Internet, Data & Web Technologies10.1007/978-3-031-53555-0_55(575-585)Online publication date: 14-Feb-2024
  • (2023)Poster: Emotional Language in News: Detection and InterventionProceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3565287.3617984(583-585)Online publication date: 23-Oct-2023
  • (2023)Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than SyntacticIEEE Access10.1109/ACCESS.2023.328154411(56921-56934)Online publication date: 2023

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