Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments
Abstract
Active and passive sensing technologies are providing powerful mechanisms to track, model, and understand a range of health behaviors and well-being states. Despite yielding rich, dense and high fidelity data, current sensing technologies often require highly engineered study designs and persistent participant compliance, making them difficult to scale to large populations and to data acquisition tasks spanning extended time periods. This paper situates social media as a new passive, unobtrusive sensing technology. We propose a semi-supervised machine learning framework to combine small samples of data gathered through active sensing, with large-scale social media data to infer mood instability (MI) in individuals. Starting from a theoretically-grounded measure of MI obtained from mobile ecological momentary assessments (EMAs), we show that our model is able to infer MI in a large population of Twitter users with 96% accuracy and F-1 score. Additionally, we show that, our model predicts self-identifying Twitter users with bipolar and borderline personality disorder to exhibit twice the likelihood of high MI, compared to that in a suitable control. We discuss the implications and the potential for integrating complementary sensing capabilities to address complex research challenges in precision medicine.
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The classifiers of support expressions are transferred from Reddit and typically performed well in our data set due to the high linguistic equivalence between Reddit and Twitter data sets.
“Similar to the symptomatic expressions classifiers, the classifiers of support expressions are transferred from Reddit and typically performed well in our data set due to the high linguistic equivalence between Reddit and Twitter data sets [34].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [34]
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Self-citation
- Authors
- Saha, K., Chan, L., De Barbaro, K., Abowd, G. D., & De Choudhury, M.
- Affiliations
- Georgia Institute of Technology (US)
- Georgia Institute of Technology
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2017-09-11
- Volume
- 1
- Issue
- 3
- Pages
- 1-27
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W2755222014
- Cited by
- 77
- References
- 84
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 5 · 2023: 5 · 2022: 9 · 2021: 13 · 2020: 17 · 2019: 16 · 2018: 4 · 2017: 1
- FWCI
- 14.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Funders
- Georgia Tech IISP Human Facing Privacy Thrust Award (3645129196)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- mood instability, social media sensing, ecological momentary assessment, passive sensing, semi-supervised learning, Twitter, bipolar disorder, borderline personality disorder, mental health prediction, precision medicine, multimodal sensing
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