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After the 2016 US presidential election, the concept of fake news captured popular attention, but conversations lacked a clear conceptualization and used the label in elastic ways to describe various distinct phenomena. In this paper, we analyze fake news as genre blending, combining elements of traditional news with features that are exogenous to normative professional journalism: misinformation, sensationalism, clickbait, and bias. Through a content analysis of stories published by 50 sites that have been labeled fake news and the engagement they generated on social media, we found that stories employed moderate levels of sensationalism, misinformation and partisanship to provide anti-establishment narratives. Complete fabrications were uncommon and did not resonate well with audiences, although there was some truth-stretching that came with genre blending. Results suggest that technocentric solutions aimed at detecting falsehoods are likely insufficient, as fake news is defined more by partisanship and identity politics than misinformation and deception.
Mourão et al. (2019) studied this question.