JOURNAL ARTICLE

Most Read Versus Most Shared: How Less (vs. More) Social Popularity Labels Influence News Media Consumption.

  • Published In: Journal of Consumer Research, 2026, v. 52, n. 5. P. 873 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Dagogo-Jack, Sokiente W; Watson, Jared 3 of 3

Abstract

The article investigates how different popularity labels used by news outlets—specifically "most read" (less social) and "most shared" (more social)—signal the relative information and entertainment value of news articles and influence consumer engagement. Across nine studies involving surveys, lab experiments, and field tests, the research finds that "most read" labels are perceived to convey higher information value and attract consumers with information-seeking motives, while "most shared" labels signal higher entertainment value and appeal to those with entertainment motives. The studies demonstrate that aligning popularity labels with consumers' dominant motives can increase click-through rates by over 20%, highlighting a strategic opportunity for media outlets to enhance audience engagement and advertising revenue. Additionally, the research suggests that temporal and contextual factors, such as choosing for oneself versus others or seasonal relevance, moderate these effects. These findings have implications for news media marketing, content promotion, and efforts to guide consumers toward credible information.

Additional Information

  • Source:Journal of Consumer Research. 2026/02, Vol. 52, Issue 5, p873
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:0093-5301
  • DOI:10.1093/jcr/ucaf017
  • Accession Number:191385609
  • Copyright Statement:Copyright of Journal of Consumer Research is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.