People lie, actions Don't! Modeling infodemic proliferation predictors among social media users

Raj, C; Meel, P

People lie, actions Don't! Modeling infodemic proliferation predictors among social media users

Raj, C

Meel, P

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Technology in Society, 2022, 68
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Raj, C https://orcid.org/0000-0003-0083-6812
dc.contributor.author	Meel, P
dc.date.accessioned	2023-04-06T03:56:53Z
dc.date.available	2023-04-06T03:56:53Z
dc.date.issued	2022-02-01
dc.identifier.citation	Technology in Society, 2022, 68
dc.identifier.issn	0160-791X
dc.identifier.issn	1879-3274
dc.identifier.uri	http://hdl.handle.net/10453/169319
dc.description.abstract	Social media is interactive, and interaction brings misinformation. With the growing amount of user-generated data, fake news on online platforms has become much more frequent since the arrival of social networks. Now and then, an event occurs and becomes the topic of discussion, generating and propagating false information. Existing literature studying fake news elaborates primarily on fake news classification models. Approaches exploring fake news characteristics to distinguish it from real news are minimal. Not much research has focused on statistical testing and generating new factor discoveries. This study assumes fifteen hypotheses to identify factors exhibiting a relationship with fake news. We perform the experiments on two real-world COVID-19 datasets using qualitative and quantitative testing methods. We determine the impact of conditional effects among sentiment, gender, and media usage. This study concludes that sentiment polarity and gender can significantly identify fake news. Dependence on the presence of visual media is, however, inconclusive. Additionally, Twitter-specific user engagement factors like followers count, friends count, favorite count, and retweet count significantly differ in fake and real news. Though, the contribution of status count is currently disputed. This study identifies practical factors to be conjunctly utilized in developing fake news detection algorithms.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Technology in Society
dc.relation.isbasedon	10.1016/j.techsoc.2022.101930
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1608 Sociology
dc.subject.classification	Business & Management
dc.title	People lie, actions Don't! Modeling infodemic proliferation predictors among social media users
dc.type	Journal Article
utslib.citation.volume	68
utslib.for	1608 Sociology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-06T03:56:52Z
pubs.publication-status	Published
pubs.volume	68

Abstract:

Social media is interactive, and interaction brings misinformation. With the growing amount of user-generated data, fake news on online platforms has become much more frequent since the arrival of social networks. Now and then, an event occurs and becomes the topic of discussion, generating and propagating false information. Existing literature studying fake news elaborates primarily on fake news classification models. Approaches exploring fake news characteristics to distinguish it from real news are minimal. Not much research has focused on statistical testing and generating new factor discoveries. This study assumes fifteen hypotheses to identify factors exhibiting a relationship with fake news. We perform the experiments on two real-world COVID-19 datasets using qualitative and quantitative testing methods. We determine the impact of conditional effects among sentiment, gender, and media usage. This study concludes that sentiment polarity and gender can significantly identify fake news. Dependence on the presence of visual media is, however, inconclusive. Additionally, Twitter-specific user engagement factors like followers count, friends count, favorite count, and retweet count significantly differ in fake and real news. Though, the contribution of status count is currently disputed. This study identifies practical factors to be conjunctly utilized in developing fake news detection algorithms.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169319