TY - GEN
T1 - Multi-emotion Recognition Using Multi-EmoBERT and Emotion Analysis in Fake News
AU - Li, Jinfen
AU - Xiao, Lu
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/4/30
Y1 - 2023/4/30
N2 - 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.
AB - 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.
KW - emotion analysis
KW - fake news detection
KW - multi-emotion recognition
UR - http://www.scopus.com/inward/record.url?scp=85159155605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159155605&partnerID=8YFLogxK
U2 - 10.1145/3578503.3583595
DO - 10.1145/3578503.3583595
M3 - Conference contribution
AN - SCOPUS:85159155605
T3 - ACM International Conference Proceeding Series
SP - 128
EP - 135
BT - WebSci 2023 - Proceedings of the 15th ACM Web Science Conference
PB - Association for Computing Machinery
T2 - 15th ACM Web Science Conference, WebSci 2023
Y2 - 30 April 2023 through 1 May 2023
ER -