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. 2022 Dec 1;17(12):e0278179.
doi: 10.1371/journal.pone.0278179. eCollection 2022.

Emotional and cognitive changes surrounding online depression identity claims

Laura Biester  1 , James Pennebaker  2 , Rada Mihalcea  1
Affiliations

Emotional and cognitive changes surrounding online depression identity claims

Laura Biester et al. PLoS One. .

Abstract

As social media has proliferated, a key aspect to making meaningful connections with people online has been revealing important parts of one's identity. In this work, we study changes that occur in people's language use after they share a specific piece of their identity: a depression diagnosis. To do so, we collect data from over five thousand users who have made such a statement, which we refer to as an identity claim. Prior to making a depression identity claim, the Reddit user's language displays evidence of increasingly higher rates of anxiety, sadness, and cognitive processing language compared to matched controls. After the identity claim, these language markers decrease and more closely match the controls. Similarly, first person singular pronoun usage decreases following the identity claim, which was previously previously found to be indicative of self-focus and associated with depression. By further considering how and to whom people express their identity, we find that the observed longitudinal changes are larger for those who do so in ways that are more correlated with seeking help (sharing in a post instead of a comment; sharing in a mental health support forum). This work suggests that there may be benefits to sharing one's depression diagnosis, especially in a semi-anonymous forum where others are likely to be empathetic.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: JP is the owner of the text analysis program LIWC. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. LIWC changes over time for users with a diagnosis identity claim and controls.
Analytical thinking is computed based on other categories; the other y axes are percentage of total words. The solid line marks the weekly mean values; the dashed line shows the trend lines for the before and after periods, and the shaded area covers the 95% confidence interval.
Fig 2
Fig 2. LIWC changes over time for users whose identity claims are in the form of posts and comments.
Analytical thinking is computed based on other categories; the other y axes are percentage of total words. The solid line marks the weekly mean values; the dashed line shows the trend lines for the before and after periods, and the shaded area covers the 95% confidence interval.
Fig 3
Fig 3. LIWC changes over time for users with whose identity claim takes place in a mental health subreddit, compared with those whose identity claim takes place in a subreddit with a different topic.
Analytical thinking is computed based on other categories; the other y axes are percentage of total words. The solid line marks the weekly mean values; the dashed line shows the trend lines for the before and after periods, and the shaded area covers the 95% confidence interval.
See this image and copyright information in PMC

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References

    1. Tigunova A, Mirza P, Yates A, Weikum G. RedDust: a Large Reusable Dataset of Reddit User Traits. In: Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France: European Language Resources Association; 2020. p. 6118–6126. Available from: https://www.aclweb.org/anthology/2020.lrec-1.751.
    1. Welch C, Kummerfeld JK, Pérez-Rosas V, Mihalcea R. Compositional Demographic Word Embeddings. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Online: Association for Computational Linguistics; 2020. p. 4076–4089. Available from: https://www.aclweb.org/anthology/2020.emnlp-main.334.
    1. Chew R, Kery C, Baum L, Bukowski T, Kim A, Navarro M. Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation. JMIR Public Health Surveill. 2021;7(3):e25807. doi: 10.2196/25807 - DOI - PMC - PubMed
    1. Coppersmith G, Dredze M, Harman C. Quantifying Mental Health Signals in Twitter. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Baltimore, Maryland, USA: Association for Computational Linguistics; 2014. p. 51–60. Available from: https://www.aclweb.org/anthology/W14-3207.
    1. Yates A, Cohan A, Goharian N. Depression and Self-Harm Risk Assessment in Online Forums. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics; 2017. p. 2968–2978. Available from: https://www.aclweb.org/anthology/D17-1322.

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