To label or not to label: The effect of stance and credibility labels on readers’ selection and perception of news articles

Mingkun Gao, Ziang Xiao, Karrie Karahalios, Wai Tat Fu

Research output: Contribution to journalArticle

Abstract

Social media sites use different labels to help users find and select news feeds. For example, Blue Feed, Red Feed, a news feed created by the Wall Street Journal, use stance labels to separate news articles with opposing political ideologies to help people explore diverse opinions. To combat the spread of fake news, Facebook has experimented with putting credibility labels on news articles to help readers decide whether the content is trustworthy. To systematically understand the effects of stance and credibility labels on online news selection and consumption, we conducted a controlled experiment to study how these labels influence the selection, perceived extremeness, and level of agreement of news articles. Results show that stance labels may intensify selective exposure - a tendency for people to look for agreeable opinions – and make people more vulnerable to polarized opinions and fake news. We found, however, that the effect of credibility labels on reducing selective exposure and recognizing fake news is limited. Although originally designed to encourage exposure to opposite viewpoints, stance labels can make fake news articles look more trustworthy, and they may lower people’s perception of the extremeness of fake news articles. Our results have important implications on the subtle effects of stance and credibility labels on online news consumption.

Original languageEnglish (US)
Article number55
JournalProceedings of the ACM on Human-Computer Interaction
Volume2
Issue numberCSCW
DOIs
StatePublished - Nov 2018

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Keywords

  • Credibility label
  • Selective exposure
  • Social opinion perception
  • Stance label

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Human-Computer Interaction
  • Computer Networks and Communications

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