research-article

A Framework for Understanding the Relationship between Social Media Discourse and Mental Health

Authors:

Mehdi Boukhechba,

Laura BarnesAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue CSCW2

Article No.: 144, Pages 1 - 23

https://doi.org/10.1145/3415215

Published: 15 October 2020 Publication History

Abstract

Over 35% of the world's population uses social media. Platforms like Facebook, Twitter, and Instagram have radically influenced the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual's personality, health, and wellbeing. To date, many researchers have employed a variety of methods for extracting mental health-centric features from digital text communication (DTC) data, including natural language processing, social network analysis, and extraction of temporal discourse patterns. However, none have explored a hierarchical framework for extracting features from private messages with the goal of unifying approaches across methodological domains. Furthermore, while analyses of large, public corpora abound in existing literature, limited work has been done to explore the relationship between of private textual communications, personality traits, and symptoms of mental illness. We present a framework for constructing rich feature spaces from digital text communications. We then demonstrate the efficacy of our framework by applying it to a dataset of private Facebook messages in a college student population (N=103). Our results reveal key individual differences in temporal and relational behaviors, as well as language usage in relation to validated measures of trait-level anxiety, loneliness, and personality. This work represents a critical step forward in linking features of private social media messages to validated measures of mental health, wellbeing, and personality.

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Index Terms

A Framework for Understanding the Relationship between Social Media Discourse and Mental Health
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue CSCW2

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October 2020

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EISSN:2573-0142

DOI:10.1145/3430143

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Cited By

Tambelli RFavieri FCasagrande M(2024)New Addictions in Late Adolescence and Emerging Adulthood: How Attachment Style May Predict Problematic Use of Social Networks and Binge-WatchingHealthcare10.3390/healthcare1205055612:5(556)Online publication date: 28-Feb-2024
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