Feeling Alone Among 317 Million Others: Disclosures of Loneliness on Twitter

Jamie Mahoney, Effie Le Moignan, Kiel Long, Manuela Barreto, Michael Wilson, Julie Barnett, John Vines, Shaun Lawson

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
19 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
JournalComputers in Human Behavior
Volume98
Early online date24 Mar 2019
DOIs
Publication statusPublished - 1 Sep 2019

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