A Tale of Two Internet News Platforms-Real vs. Fake: An Elaboration Likelihood Model Perspective

Abstract

This paper presents findings from a field analysis of real vs. fake news propagated on the Internet. Elaboration Likelihood Model (ELM) was used as a theoretical framework to investigate information presentation mechanisms used by real and fake content generators to persuade readers. ELM theorizes two routes through which information can inform attitudinal changes: a central route of high cognitive effort, and a peripheral route of low cognitive effort. We hypothesize that fake news sites favor the peripheral route by providing less information overall, and by providing more negative affective cues. Data was gathered from Internet platforms that publish real news and fake news. Results indicate that the amount of information disseminated by fake news platforms is lower than that of reputable platforms. Content analysis reveals that fake news with business impact are typically more negative in their valence compared to real news. Implications of our findings for theory and practice are discussed.