Who says what?

• Verfasst von: Feldhege, Johannes [VerfasserIn]
• Verfasst von: Moessner, Markus [VerfasserIn]
• Verfasst von: Bauer, Stephanie [VerfasserIn]
• Titel: Who says what?
• Titelzusatz: Content and participation characteristics in an online depression community
• Verf.angabe: Johannes Feldhege, Markus Moessner, Stephanie Bauer
• Jahr: 2020
• Umfang: 7 S.
• Fussnoten: Available online 04 November 2019 ; Gesehen am 07.05.2020
• Titel Quelle: Enthalten in: Journal of affective disorders
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1979
• Jahr Quelle: 2020
• Band/Heft Quelle: 263(2020), Seite 521-527
• ISSN Quelle: 1573-2517
• DOI: doi:10.1016/j.jad.2019.11.007
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 169770851X

Abstract

Background - An increasingly important source of informal help for people with depression are online depression communities. This study investigates the prevailing topics in an online depression community and how they are related to participation styles. - Methods - A topic model with 26 topics of N=16,291 posts and N=71,543 comments of N=20,037 users in a depression forum on Reddit was created using Latent Dirichlet allocation (LDA). The topics’ proportions in the corpus were correlated with five participation measures, i.e. sum of scores, number of comments, posts to comments ratio, posting frequency, and word count. - Results - The most common topics were Feelings, Motivation, The Community on Reddit, and Time. There were many significant, small to moderate correlations between topic proportions and participation style measures. The topics Feelings, Offering Support, and Small Talk generated a bigger response in the form of scores and comments. Talking about the past and relationships was more common in longer posts, whereas small talk, offering emotional support, and employing cognitive strategies was more readily found in short comments. Lower posting frequency was related to talking about feelings and romantic relationships. - Limitations - No information on users’ demographics or mental health status was available. Topic modeling cannot capture elements of style and tone of text. - Conclusions - A wide spectrum of topics was uncovered in the topic modeling. Patterns in the correlations point to users with different participation styles preferring different topics. Results of this study can aid the development of online interventions for depression.