Progress and challenges in the analysis of big data in social media of adolescents

Schmidt, Stefanie Julia; Kaess, Michael (2018). Progress and challenges in the analysis of big data in social media of adolescents (In Press). Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie, pp. 1-10. Huber 10.1024/1422-4917/a000623

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Progress and challenges in the analysis of big data in social media of adolescents Abstract. Social media are ubiquitous today, and adolescents use them to express their thoughts, feelings, and behaviours. New interdisciplinary methods allow the automatic analysis of the massive amounts of data (big data) available on social networking websites using machine-learning tools to detect indicators of mental-health problems and disorders by identifying differences with common activity and communication patterns. This review first introduces the concept and potential fields of applications of big data in social media. It then discusses the first studies that used big data analyses and detected mental-health problems by identifying differences in the structure of social networks, in the use of certain words, and in the communication of opinions and sentiments. Future studies employing several assessment points could use longitudinal mediation analysis to model intraindividual changes in order to understand when and through which mechanisms social media use has an impact on mental health. Furthermore, future studies should include additional mental disorders, various sources of information, a broader age range, and additional social-networking websites to develop more precise models for the early detection of mental disorders. This would enable the development of personalised intervention programs to promote mental health and resilience in adolescents.

Item Type:

Journal Article (Further Contribution)

Division/Institute:

04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy
07 Faculty of Human Sciences > Institute of Psychology > Clinical Psychology and Psychotherapy

UniBE Contributor:

Schmidt, Stefanie Julia and Kaess, Michael

Subjects:

600 Technology > 610 Medicine & health
100 Philosophy > 150 Psychology

ISSN:

1422-4917

Publisher:

Huber

Language:

German

Submitter:

Dania Gioia Spagnuolo

Date Deposited:

12 Apr 2019 10:20

Last Modified:

22 Oct 2019 19:42

Publisher DOI:

10.1024/1422-4917/a000623

PubMed ID:

30375920

Uncontrolled Keywords:

Adoleszenz Big Data Digitalisierung Früherkennung Prävention adolescence big data digital early detection prevention social media soziale Medien

BORIS DOI:

10.7892/boris.126801

URI:

https://boris.unibe.ch/id/eprint/126801

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