Who Says What in Which Networks: What influences Social Media Users’ Emotional Reactions to the COVID-19 Vaccine Infodemic? DOI
Aimei Yang, Jieun Shin, Hye Min Kim

et al.

Social Science Computer Review, Journal Year: 2022, Volume and Issue: 41(6), P. 1986 - 2009

Published: Sept. 24, 2022

This study aims to identify effective predictors that influence publics’ emotional reactions COVID-19 vaccine misinformation as well corrective messages. We collected a large sample of related and messages on Facebook the users’ (i.e., emojis) these Focusing three clusters features such messages’ linguistic features, source characteristics, network positions, we examined whether information would differ. used random forest models most salient among over 70 for both types Our analysis found misinformation, political ideology message was feature predicted anxious enthusiastic reactions, followed by highlight personal concerns positions. For messages, while sources’ still key raising anxiety, important triggering enthusiasm positions quality.

Language: Английский

Emotion aided multi-task framework for video embedded misinformation detection DOI
Rina Kumari, Vipin Gupta, Nischal Ashok

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(12), P. 37161 - 37185

Published: Oct. 19, 2023

Language: Английский

Citations

7
LIMFA: label-irrelevant multi-domain feature alignment-based fake news detection for unseen domain DOI
Danke Wu, Zhenhua Tan, Haoran Zhao

et al.

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 36(10), P. 5197 - 5215

Published: Dec. 27, 2023

Language: Английский

Citations

6
Fake News, Real Emotions: Emotion Analysis of COVID-19 Infodemic in Weibo DOI Creative Commons
Mingyu Wan, Yin Zhong, Xuefeng Gao

et al.

IEEE Transactions on Affective Computing, Journal Year: 2023, Volume and Issue: 15(3), P. 815 - 827

Published: July 17, 2023

The proliferation of COVID-19 fake news on social media poses a severe threat to the health information ecosystem. We show that affective computing can make significant contributions combat this infodemic. Given is often presented with emotional appeals, we propose new perspective role emotion in attitudes, perceptions, and behaviors dissemination information. study emotions conjunction news, explore different aspects their interaction. To process both 'falsehood' based same set data, auto-tag existing datasets following an established taxonomy. More specifically, distribution seven basic (e.g. Happiness, Like, Fear, Sadness, Surprise, Disgust, Anger ), find across domains styles dominated by xmlns:xlink="http://www.w3.org/1999/xlink">Fear (e.g., coronavirus), xmlns:xlink="http://www.w3.org/1999/xlink">Disgust conflicts). In addition, framing terms gain-versus-loss reveals close correlation between emotions, collective human reactions. Our analysis confirms spreading especially when contextualized loss frame. points future direction incorporating footprints models automatic detection, establishes approach quality general detection particular.

Language: Английский

Citations

4
SciND: a new triplet-based dataset for scientific novelty detection via knowledge graphs DOI
Komal Gupta,

Ammaar Ahmad,

Tirthankar Ghosal

et al.

International Journal on Digital Libraries, Journal Year: 2024, Volume and Issue: 25(4), P. 639 - 659

Published: Jan. 8, 2024

Language: Английский

Citations

1
Who Says What in Which Networks: What influences Social Media Users’ Emotional Reactions to the COVID-19 Vaccine Infodemic? DOI
Aimei Yang, Jieun Shin, Hye Min Kim

et al.

Social Science Computer Review, Journal Year: 2022, Volume and Issue: 41(6), P. 1986 - 2009

Published: Sept. 24, 2022

This study aims to identify effective predictors that influence publics’ emotional reactions COVID-19 vaccine misinformation as well corrective messages. We collected a large sample of related and messages on Facebook the users’ (i.e., emojis) these Focusing three clusters features such messages’ linguistic features, source characteristics, network positions, we examined whether information would differ. used random forest models most salient among over 70 for both types Our analysis found misinformation, political ideology message was feature predicted anxious enthusiastic reactions, followed by highlight personal concerns positions. For messages, while sources’ still key raising anxiety, important triggering enthusiasm positions quality.

Language: Английский

Citations

7