Investigating the emotional appeal of fake news using artificial intelligence and human contributions

By Jeannette Paschen. 2019

93 citations recorded by the source corpus.

Purpose The creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the “fake news crisis”. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, to support managers in identifying fake news. The study in this paper demonstrates how AI with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Using an AI application, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers. Design/methodology/approach The study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content. Findings The results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy. Originality/value This is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Trust can be evaluated in the category of information, such as information about democratic and policy institutions [177].”
Fake news, disinformation and misinformation in social media: a review (2023, unknown): “18 Besides, recent studies have shown that the power of AI algorithms for identifying fake news is lower than its ability to create it Paschen (2019).”
Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023, background): “” Prior work has found that misinformation was signifcantly more emotional and less neutral in sentiment than non-misinformation [82].”