Fortschritte und Herausforderungen für die Analyse von Big Data in sozialen Medien im Jugendalter
Abstract
Zusammenfassung. Für Jugendliche sind soziale Medien allgegenwärtig und sie verwenden sie, um ihren Gedanken, Gefühlen und Verhaltensweisen Ausdruck zu verleihen. Entsprechend bietet sich mit neuen interdisziplinären Methoden die Möglichkeit, die in sozialen Netzwerken vorhandenen Massendaten (Big Data) automatisch und maschinell zu analysieren, um darin Indikatoren für psychische Auffälligkeiten und Störungen im Sinne von Abweichungen von den üblichen Aktivitäts- und Kommunikationsmustern zu identifizieren. Diese Übersichtsarbeit gibt zunächst eine Einführung in das Konzept und mögliche Anwendungsbereiche von Big Data in sozialen Medien. Darauf aufbauend werden die ersten Studien diskutiert, die mittels dieser Analysen psychische Auffälligkeiten im Jugendalter entdecken konnten, da sich Unterschiede in der Struktur der sozialen Netzwerke, in der Verwendung von Wörtern und in der Kommunikation von Meinungen und Gefühlen fanden. Der Einbezug einer Vielzahl von Messzeitpunkten für die Modellierung intraindividueller Veränderungen könnte künftig in Kombination mit Mediatoranalysen helfen, besser zu verstehen, wann und durch welche Mechanismen sich der Konsum sozialer Medien auf die psychische Gesundheit auswirkt. Künftige Studien sollten zudem durch die Berücksichtigung weiterer Störungsbilder und Informationsquellen, verschiedener Altersgruppen und zusätzlicher sozialer Netzwerke zur Entwicklung von genaueren Prädiktionsmodellen zur Früherkennung psychischer Störungen in dieser Altersgruppe beitragen und darauf abgestimmte personalisierte Interventionen zur Förderung der psychischen Gesundheit und Resilienz anbieten.
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.
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