Quantifying Mental Health Signals in Twitter

Coppersmith, Glen; Dredze, Mark; Harman, Craig

doi:10.3115/v1/w14-3207

Cited by 542 publications

(526 citation statements)

References 31 publications

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“…Data for the shared task are comprised of public tweets collected according to the procedures of Coppersmith et al (2014a). We briefly describe the procedure here, and refer interested readers to Coppersmith et al (2014a).…”

Section: Shared Task Datamentioning

confidence: 99%

“…The 2015 CLPsych Shared Task consisted of three user-level binary classification tasks: PTSD vs. control, depression vs. control, and PTSD vs. depression. The first two have been addressed in a number of settings (Coppersmith et al, 2015;Coppersmith et al, 2014b;Coppersmith et al, 2014a;Resnik et al, 2013;De Choudhury et al, 2013;Rosenquist et al, 2010;Ramirez-Esparza et al, 2008), while the third task is novel. Organizing this shared task brought together many teams to consider the same problem, which had the benefit of establishing a solid foundational understanding, common standards, and a shared deep understanding of both task and data.…”

Section: Introductionmentioning

confidence: 99%

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CLPsych 2015 Shared Task: Depression and PTSD on Twitter

Coppersmith¹,

Dredze

Harman

et al. 2015

Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Realit

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This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.

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Section: Shared Task Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CLPsych 2015 Shared Task: Depression and PTSD on Twitter

Coppersmith¹,

Dredze

Harman

et al. 2015

Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Realit

Self Cite

245

199

View full text Add to dashboard Cite

show abstract

“…Late night activity can be an indication of unusual rhythms in sleep (insomnia and hypersomnia) [6] and can predict future episodes of depression. In addition to the time feature, we address the text of the post (text score), which holds the most vital information pertaining to an individual's current mood and mental health [30].…”

Section: Suicide Warning Signs In Online Behaviormentioning

confidence: 99%

Detection of suicide-related posts in Twitter data streams

Vioulès¹,

Moulahi²,

Azé³

et al. 2018

IBM J. Res. & Dev.

120

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Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information spread across social media platforms holds valuable indicators to effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide-warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.

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“…by correlating language use characteristics with traits of the person producing the language). Much of this work has been done using Facebook and Twitter (Coppersmith et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

DLATK: Differential Language Analysis ToolKit

Schwartz¹,

Giorgi²,

Sap³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

122

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We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.

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Quantifying Mental Health Signals in Twitter

Cited by 542 publications

References 31 publications

CLPsych 2015 Shared Task: Depression and PTSD on Twitter

CLPsych 2015 Shared Task: Depression and PTSD on Twitter

Detection of suicide-related posts in Twitter data streams

DLATK: Differential Language Analysis ToolKit

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