Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments
Affect Affective Instability EMA Ecological Momentary Assessments Health Mental Well-Being Mood Mood Instability Social media Twitter
Koustuv Saha Larry Chan Kaya De Barbaro Gregory D. Abowd Munmun De Choudhury AbowdGregory D De ChoudhuryMunmun De BarbaroKaya
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 ...
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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
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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Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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Author Tags

Affect

Affective Instability

EMA

Ecological Momentary Assessments

Health

Mental Well-Being

Mood

Mood Instability

Social media

Twitter

Qualifiers

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Research

Refereed

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Georgia Tech IISP Human Facing Privacy Thrust Award

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Di Cara NMaggio VDavis OHaworth CMethodologies for Monitoring Mental Health on Twitter: Systematic ReviewJournal of Medical Internet Research10.2196/4273425(e42734)

Han NLi SHuang FWen YWang XLiu XLi LZhu TSensing Psychological Well-being Using Social Media Language: Prediction Model Development StudyJournal of Medical Internet Research10.2196/4182325(e41823)

Renault HFreeman CBanica ISandre AEthridge PPark JWeinberg ANeural response to rewards moderates the within‐person association between daily positive events and positive affect during a period of stress exposurePsychophysiology10.1111/psyp.1437660:12

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Affiliations

Koustuv Saha

Georgia Institute of Technology

Larry Chan

Georgia Institute of Technology

Kaya De Barbaro

Georgia Institute of Technology

Gregory D. Abowd

Georgia Institute of Technology

Munmun De Choudhury

Georgia Institute of Technology

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