Markus Moessner1, Johannes Feldhege1, Markus Wolf2, Stephanie Bauer1. 1. Center for Psychotherapy Research, University Hospital Heidelberg, Heidelberg, Germany. 2. Department of Psychology, University of Zurich, Zurich, Switzerland.
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.
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.
Authors: Kathryn E Smith; Tyler B Mason; Adrienne Juarascio; Lauren M Schaefer; Ross D Crosby; Scott G Engel; Stephen A Wonderlich Journal: Int J Eat Disord Date: 2019-07-16 Impact factor: 4.861
Authors: Christophe Lemey; Aziliz Le Glaz; Yannis Haralambous; Deok-Hee Kim-Dufor; Philippe Lenca; Romain Billot; Taylor C Ryan; Jonathan Marsh; Jordan DeVylder; Michel Walter; Sofian Berrouiguet Journal: J Med Internet Res Date: 2021-05-04 Impact factor: 5.428
Authors: Patricia Sánchez-Herrera-Baeza; Roberto Cano-de-la-Cuerda; Edwin Daniel Oña-Simbaña; Domingo Palacios-Ceña; Jorge Pérez-Corrales; Juan Nicolas Cuenca-Zaldivar; Javier Gueita-Rodriguez; Carlos Balaguer-Bernaldo de Quirós; Alberto Jardón-Huete; Alicia Cuesta-Gomez Journal: Sensors (Basel) Date: 2020-04-11 Impact factor: 3.576
Authors: Pilar Aparicio-Martinez; Alberto-Jesus Perea-Moreno; María Pilar Martinez-Jimenez; María Dolores Redel-Macías; Claudia Pagliari; Manuel Vaquero-Abellan Journal: Int J Environ Res Public Health Date: 2019-10-29 Impact factor: 3.390