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

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journal contribution
posted on 2019-07-18, 15:41 authored by Jamie Mahoney, Effie Le Moignan, Kiel Long, Michael WilsonMichael Wilson, Julie Barnett, John Vines, Shaun Lawson
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.


RCUK grant ES/M003558/1, funded through the Empathy and Trust in Online Communicating (EMoTICON) funding call administered by the Economic and Social Research Council in conjunction with the RCUK Connected Communities, Digital Economy and Partnership for Conflict, Crime and Security themes, and supported by the Defence Science and Technology Laboratory (Dstl) and Centre for the Protection of National Infrastructure (CPNI).



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Published in

Computers in Human Behavior




20 - 30


MAHONEY, J. ... et al, 2019. Feeling alone among 317 million others: Disclosures of loneliness on Twitter. Computers in Human Behavior, 98, pp.20-30.


© Elsevier


  • AM (Accepted Manuscript)

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This paper was accepted for publication in the journal Computers in Human Behavior and the definitive published version is available at https://doi.org/10.1016/j.chb.2019.03.024.

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