Quantifying the Language of Schizophrenia in Social Media

Mitchell, Margaret; Hollingshead, Kristy; Coppersmith, Glen

doi:10.3115/v1/w15-1202

Cited by 137 publications

(139 citation statements)

References 22 publications

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“…Written text carries implicit information about the author, a relationship that has been exploited in natural language processing (NLP) to predict author characteristics, such as age (Goswami et al, 2009;Rosenthal and McKeown, 2011;Nguyen et al, 2011;Nguyen et al, 2014), gender (Sarawgi et al, 2011;Ciot et al, 2013;Liu and Ruths, 2013;Alowibdi et al, 2013;Volkova et al, 2015;Hovy, 2015), personality and stance (Schwartz et al, 2013b;Schwartz et al, 2013a;Volkova et al, 2014;Plank and Hovy, 2015;Preoţiuc-Pietro et al, 2015), or occupation (Preotiuc-Pietro et al, 2015a;Preoţiuc-Pietro et al, 2015b). The same signal has also been effectively used to predict mental health conditions, such as depression (Coppersmith et al, 2015b;Schwartz et al, 2014), suicidal ideation (Coppersmith et al, 2016;Huang et al, 2015), schizophrenia (Mitchell et al, 2015) or post-traumatic stress disorder (PTSD) (Pedersen, 2015), often more accurately than by traditional diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Multitask Learning for Mental Health Conditions with Limited Social Media Data

Benton¹,

Mitchell²,

Hovy³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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150

135

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Language contains information about the author's demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.

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Section: Introductionmentioning

confidence: 99%

Multitask Learning for Mental Health Conditions with Limited Social Media Data

Benton¹,

Mitchell²,

Hovy³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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150

135

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“…The average number of tweets per user is around 2,800. Detailed information on this dataset can be found in (Mitchell et al, 2015). Below are some tweets from this dataset (they have been rephrased to preserve anonymity): …”

Section: Datasetmentioning

confidence: 99%

Predictive Linguistic Features of Schizophrenia

Kayi¹,

Diab²,

Pauselli³

et al. 2017

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

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Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.

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“…Many groups have already validated this paradigm (Roark et al, 2011;Fraser et al, 2014;Resnik et al, 2013;Lehr et al, 2012;Fraser et al, 2016;Mitchell et al, 2015). First, Speech Graphs has been used in different pathologies (schizophrenic and bipolar), results are published in (Carrillo et al, 2014;Mota et al, , 2012.…”

Section: Preliminary Resultsmentioning

confidence: 99%

Computational Characterization of Mental States: A Natural Language Processing Approach

Carrillo

2017

Proceedings of ACL 2017, Student Research Workshop

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Psychiatry is an area of medicine that strongly bases its diagnoses on the psychiatrists subjective appreciation. The task of diagnosis loosely resembles the common pipelines used in supervised learning schema. Therefore, we propose to augment the psychiatrists diagnosis toolbox with an artificial intelligence system based on natural language processing and machine learning algorithms. This approach has been validated in many works in which the performance of the diagnosis has been increased with the use of automatic classification.

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Quantifying the Language of Schizophrenia in Social Media

Cited by 137 publications

References 22 publications

Multitask Learning for Mental Health Conditions with Limited Social Media Data

Multitask Learning for Mental Health Conditions with Limited Social Media Data

Predictive Linguistic Features of Schizophrenia

Computational Characterization of Mental States: A Natural Language Processing Approach

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