Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

Article


Nanath, K., Kaitheri, S., Malik, S. and Mustafa, S. 2022. Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments. Journal of Systems and Information Technology. 24 (2), pp. 131-155. https://doi.org/10.1108/jsit-11-2020-0257
TypeArticle
TitleExamination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments
AuthorsNanath, K., Kaitheri, S., Malik, S. and Mustafa, S.
Abstract

Purpose
The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.
Design/methodology/approach
A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.
Findings
The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.
Practical implications
Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.
Originality/value
While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

KeywordsSentiment Analysis; Topic Modeling; Fake News; Emotion-focussed content; Fake content prediction; Natural language processing
Sustainable Development Goals11 Sustainable cities and communities
Middlesex University ThemeSustainability
Research GroupArtificial Intelligence group
PublisherEmerald
JournalJournal of Systems and Information Technology
ISSN1328-7265
Electronic1758-8847
Publication dates
Online14 Jan 2022
Print11 Apr 2022
Publication process dates
Accepted14 Jan 2022
Deposited30 Oct 2023
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1108/jsit-11-2020-0257
Scopus EID2-s2.0-85122818055
LanguageEnglish
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