Recognizing Depression from Twitter Activity

By Sho Tsugawa, Yusuke Kikuchi, Fumio Kishino, Kosuke Nakajima, Yuichi Itoh, Hiroyuki Ohsaki. International Conference on Human Factors in Computing Systems, 2015

326 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3b4b7917e830ca181f3ae946f7f8117d97ab9a0a

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Citations in the corpus

Depression and Self-Harm Risk Assessment in Online Forums (2017, background): “There is a growing body of related work analyzing mental health-related discourse and language usage in social media to better discover and understand mental health related concerns (Resnik et al., 2013; De Choudhury et al., 2013; Coppersmith et al., 2014b,a; Mitchell et al., 2015; Tsugawa et al., 2”
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media (2016, unknown): “Linguistic attributes of shared content and social interactional patterns have been utilized to understand and infer risk to major depressive disorder [24, 49, 32, 16, 60, 70], postpartum depression [21, 22], addiction [47, 44], and other mental health concerns [35, 18, 17, 46].”
Detecting depression and mental illness on social media: an integrative review (2017, methodology): “[12] predicted depression from Twitter data in a Japanese sample, using the CES-D as their assessment criterion.”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “Since then, researchers have proposed a wide range of feature engineering methods and built machine-learning models for the prediction [9, 54, 56, 83].”
Detection of Depression-Related Posts in Reddit Social Media Forum (2019, background): “Some other papers compare the performance of individual features with various machine learning classifiers [9]–[12].”
Methods in predictive techniques for mental health status on social media: a critical review (2020, methodology): “We also saw the use of decision trees (2)35,66, Naïve Bayes (2)31,82, and XGBoost49.”
The Geography and Importance of Localness in Geotagged Social Media (2016, background): “Indeed, researchers in HCI (e.g., [1,9,45,50]), the social sciences (e.g., [10,14,21,39,46,51]), and even the natural sciences (e.g., [38,44,49]) now regularly use social media VGI to better understand phenomena of interest ranging from social unrest and emergencies [6,24,43] to disease tracking…”
Social Networking Sites, Depression, and Anxiety: A Systematic Review (2016, background): “In addition to language features, the time of posting, relative volume of posts, and reciprocity (likes and comments, tweets and retweets) may also aid in describing individuals with and without depression, with depression correlating with more night activity and less volume and reciprocity than non”