Work2015

Recognizing Depression from Twitter Activity

Sho Tsugawa · Yusuke Kikuchi · Fumio Kishino · Kosuke Nakajima · Yuichi Itoh · Hiroyuki Ohsaki

349 citations0/0 files storedCHI Conference on Human Factors in Computing Systems

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Mental Health via Writing Sentiment Analysis and Opinion Mining Emotion and Mood Recognition

Abstract

In this paper, we extensively evaluate the effectiveness of using a user's social media activities for estimating degree of depression. As ground truth data, we use the results of a web-based questionnaire for measuring degree of depression of Twitter users. We extract several features from the activity histories of Twitter users. By leveraging these features, we construct models for estimating the presence of active depression. Through experiments, we show that (1) features obtained from user activities can be used to predict depression of users with an accuracy of 69%, (2) topics of tweets estimated with a topic model are useful features, (3) approximately two months of observation data are necessary for recognizing depression, and longer observation periods do not contribute to improving the accuracy of estimation for current depression; sometimes, longer periods worsen the accuracy.

Citations in this library

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References

1977The CES-D Scale 1961An Inventory for Measuring Depression 1965A Self-Rating Depression Scale 1967Development of a Rating Scale for Primary Depressive Illness 2011Twitter mood predicts the stock market 2010Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment 2021Predicting Depression via Social Media 2012Depression: A Global Public Health Concern

Cited by

2024Mental-LLM 2017Researching Mental Health Disorders in the Era of Social Media: Systematic Review 2019A text classification framework for simple and effective early depression detection over social media streams 2022Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media 2020Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features 2020Application of Machine Learning Methods in Mental Health Detection: A Systematic Review

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