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

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.

Exploring the relationships between emotions in fake news

“…The reasons behind this reduction could be multifaceted, encompassing the inherently controversial or alarming nature of fake news, which may overshadow positive elements. We note that these results also align with previous findings on fake news (Nanath et al, 2022;Corbu et al, 2021).…”

The influence of emotions on online information sharing behavior

“…In this sense, a positive emotional state heightens their socialization behavior while allowing them to view the quality of information circulating on social media in a more positive light, leading them to share fake news, whereas negative emotions influence information overload and prior information beliefs, which in turn increase fake news sharing. As such it distinguishes itself from previous studies that have focused on possible social gains (Kozinets et al , 2020), trust in online channels (Khan and Idris, 2019; Talwar et al , 2019), information overload (Bermes, 2021) and virality (Nanath et al , 2022) to provide explanations for the sharing of fake news online.…”

The effects of news authenticity and social media tie strength on consumer dissemination behavior

“…Wardle (2017) also identified seven types of fake news, including satire/parody, false connection, misleading content, false content, imposter content, manipulated content, and fabricated content. Prior research in this area has focused primarily on the technical and political aspects of fake news (e.g., Buchanan & Kempley, 2021;Nanath et al, 2022;Rao et al, 2021). For example, Shin et al (2018) and Parikh et al (2019) used keyword strings, graphics, video and audio content, and information sources as factors to measure the degree to which fake news diverged from the "truth" and to evaluate the credibility of news in order to reduce the spread of false information.…”

“…The 2018 statistics released by Taiwan FactCheck Center also show politics to be the dominant category of media‐disseminated false information, with 64 pieces, followed by commercial activities, with 42 pieces (Liu et al, 2019). However, previous studies focused mainly on the detection of fake news (e.g., Nanath et al, 2022; Rao et al, 2021) or the political aspects of fake news (e.g., Buchanan & Kempley, 2021), with less attention paid to the effects of social media misinformation in the context of online marketing and consumer behavior (Apuke & Omar, 2021; Domenico et al, 2021; Mishra & Samu, 2021; Sharif et al, 2022).…”

“…However, previous studies focused mainly on the detection of fake news (e.g., Nanath et al, 2022;Rao et al, 2021) or the political aspects of fake news (e.g., Buchanan & Kempley, 2021), with less attention paid to the effects of social media misinformation in the context of online marketing and consumer behavior (Apuke & Omar, 2021;Domenico et al, 2021;Mishra & Samu, 2021;Sharif et al, 2022).…”