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Verfasst von:Moessner, Markus [VerfasserIn]   i
 Feldhege, Johannes [VerfasserIn]   i
 Wolf, Markus [VerfasserIn]   i
 Bauer, Stephanie [VerfasserIn]   i
Titel:Analyzing big data in social media
Titelzusatz:text and network analyses of an eating disorder forum
Verf.angabe:Markus Moessner, Johannes Feldhege, Markus Wolf, Stephanie Bauer
E-Jahr:2018
Jahr:10 May 2018
Umfang:12 S.
Fussnoten:Gesehen am 07.05.2020
Titel Quelle:Enthalten in: The international journal of eating disorders
Ort Quelle:New York, NY : Wiley, 1981
Jahr Quelle:2018
Band/Heft Quelle:51(2018), 7, Seite 656-667
ISSN Quelle:1098-108X
Abstract:Objective Social media plays an important role in everyday life of young people. Numerous studies claim negative effects of social media and media in general on eating disorder risk factors. Despite the availability of big data, only few studies have exploited the possibilities so far in the field of eating disorders. Method Methods for data extraction, computerized content analysis, and network analysis will be introduced. Strategies and methods will be exemplified for an ad-hoc dataset of 4,247 posts and 34,118 comments by 3,029 users of the proed forum on Reddit. Results Text analysis with latent Dirichlet allocation identified nine topics related to social support and eating disorder specific content. Social network analysis describes the overall communication patterns, and could identify community structures and most influential users. A linear network autocorrelation model was applied to estimate associations in language among network neighbors. The supplement contains R code for data extraction and analyses. Discussion This paper provides an introduction to investigating social media data, and will hopefully stimulate big data social media research in eating disorders. When applied in real-time, the methods presented in this manuscript could contribute to improving the safety of ED-related online communication.
DOI:doi:10.1002/eat.22878
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1002/eat.22878
 Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/eat.22878
 DOI: https://doi.org/10.1002/eat.22878
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:big data
 eating disorders
 social media
 social network analysis
 text analysis
K10plus-PPN:169764631X
Verknüpfungen:→ Zeitschrift

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