Modeling Stress with Social Media Around Incidents of Gun Violence on College Campuses
Abstract
Stress constitutes a persistent wellbeing challenge to college students, impacting their personal, social, and academic life. However, violent events on campuses may aggravate student stress, due to the induced fear and trauma. In this paper, leveraging social media as a passive sensor of stress, we propose novel computational techniques to quantify and examine stress responses after gun violence on college campuses. We first present a machine learning classifier for inferring stress expression in Reddit posts, which achieves an accuracy of 82%. Next, focusing on 12 incidents of campus gun violence in the past five years, and social media data gathered from college Reddit communities, our methods reveal amplified stress levels following the violent incidents, which deviate from usual stress patterns on the campuses. Further, distinctive temporal and linguistic changes characterize the campus populations, such as reduced cognition, higher self pre-occupation and death-related conversations. We discuss the implications of our work in improving mental wellbeing and rehabilitation efforts around crisis events in college student populations.
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Psycholinguistic expressions that signal an individual's underlying psychological state are associated with non-content words, which are rarely consciously regulated.
“Moreover, psycholinguistic expressions that signal an individual ' s underlying psychological state, are associated with non-content words, such as articles and pronouns, which are rarely consciously regulated by the individual and may span a variety of topics directly or indirectly related to a crisis, whether of fl ine (Cohn et al., 2004) or online (Saha and De Choudhury, 2017).”
From Impacts of school shooter drills on the psychological well-being of American K-12 school communities: a social media study (2021) · cites this paper as Saha and De Choudhury, 2017
LIWC is known to work well with short text and social media data.
“This tool is known to work well with short text and social media data, as revealed in a large body of social computing and crisis informatics literature (Lin and Margolin, 2014; Mark et al., 2012; Saha and De Choudhury, 2017).”
From Impacts of school shooter drills on the psychological well-being of American K-12 school communities: a social media study (2021) · cites this paper as Saha and De Choudhury, 2017
Saha et al's classifiers used here have been shown to transfer well on Twitter with an 87% agreement between machine-predicted labels and expert appraisal.
“Saha et al’s [25] classifiers used here have also been shown to transfer well on Twitter with an 87% agreement between machine-predicted labels and expert appraisal [48], where experts annotated posts in the classification test data using Diagnostic and Statistical Manual of Mental Disorders, 5th Edition [55] criteria of mental health symptoms.”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [48]
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References 93 · Cited by 124
- Authors
- Saha, K., & De Choudhury, M.
- Affiliations
- Georgia Institute of Technology (US)
- Georgia Institute of Technology, Atlanta, GA, USA
- ORCID
- Saha, Koustuv
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2017-12-06
- Volume
- 1
- Issue
- CSCW
- Pages
- 1-27
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W2774842745
- Cited by
- 124
- References
- 93
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 14 · 2023: 12 · 2022: 19 · 2021: 23 · 2020: 18 · 2019: 19 · 2018: 7
- FWCI
- 17.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- Funders
- National Institutes of Health (1R01GM11269701)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- stress detection, university students, social media, Reddit posts, passive sensing, psychological well-being, crisis events, self-absorption
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