Feeling alone among 317 million others: Disclosures of loneliness on Twitter

Jamie Mahoney, Effie Le Moignan, Kiel Long, Mike Wilson, Julie Barnett, John Vines, Shaun Lawson

Research output: Contribution to journalArticle

6 Citations (Scopus)
31 Downloads (Pure)

Abstract

Increasing numbers of individuals describe themselves as feeling lonely, regardless of age, gender or geographic location. This article investigates how social media users self-disclose feelings of loneliness, and how they seek and provide support to each other. Motivated by related studies in this area, a dataset of 22,477 Twitter posts sent over a one-week period was analyzed using both qualitative and quantitative methods. Through a thematic analysis, we demonstrate that self-disclosure of perceived loneliness takes a variety of forms, from simple statements of “I'm lonely”, through to detailed self-reflections of the underlying causes of loneliness. The analysis also reveals forms of online support provided to those who are feeling lonely. Further, we conducted a quantitative linguistic content analysis of the dataset which revealed patterns in the data, including that ‘lonely’ tweets were significantly more negative than those in a control sample, with levels of negativity fluctuating throughout the week and posts sent at night being more negative than those sent in the daytime.

Original languageEnglish
Pages (from-to)20-30
Number of pages11
JournalComputers in Human Behavior
Volume98
Early online date24 Mar 2019
DOIs
Publication statusPublished - 30 Sep 2019

Keywords

  • Loneliness
  • Self-disclosure
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

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