Loneliness and social media.

  • Published In: Annals of the New York Academy of Sciences, 2025, v. 1543, n. 1. P. 5 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hall, Jeffrey A. 3 of 3

Abstract

After defining five possible pathways to increase belonging through social media use, this narrative review summarizes the research on social media and loneliness. The association between social media use and loneliness is examined at the trait level and change in loneliness over time. Next, the use of social media during the COVID pandemic and its use to increase belonging at the momentary or daily level are summarized. Following, the use of social media to cope with loneliness or ostracism as well as the social compensation and enhancement hypotheses are examined. The evidence suggests social media use is weakly related to trait loneliness, explains little variance in loneliness relative to other predictors, and fails to explain a change in loneliness over time. There is no evidence it causes loneliness. On any given day, however, social media use may be used to promote belongingness but may not be a good means of coping with loneliness in the long term. This narrative review concludes that future research should firmly situate the study of loneliness and social media in the context of social interactions and relationships by carefully examining when and for whom it is beneficial or harmful. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Annals of the New York Academy of Sciences. 2025/01, Vol. 1543, Issue 1, p5
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2025
  • ISSN:0077-8923
  • DOI:10.1111/nyas.15275
  • Accession Number:183923402
  • Copyright Statement:Copyright of Annals of the New York Academy of Sciences is the property of Wiley-Blackwell 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.