Feature Engineering for Depression Detection in Social Media

Stankevich, Maxim; Isakov, Vadim; Devyatkin, Dmitry; Smirnov, Ivan V.

doi:10.5220/0006598604260431

Cited by 49 publications

(25 citation statements)

References 5 publications

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“…Of the 36 mental health classification studies, 14 (39%) studies incorporated external mental health data sets into data labeling procedures to support the ground truth of classification. External data set sources ranged from Wikipedia [ 36 ], Twitter [ 37 ], and AskAPatient [ 65 ] to formalized medical sources, including the Unified Medical Language System [ 31 ], the International Classification of Diseases, 10th Revision [ 48 ], and the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders [ 48 , 69 ].…”

Section: Resultsmentioning

confidence: 99%

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Boettcher¹

2021

JMIR Ment Health

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Background The study of depression and anxiety using publicly available social media data is a research activity that has grown considerably over the past decade. The discussion platform Reddit has become a popular social media data source in this nascent area of study, in part because of the unique ways in which the platform is facilitative of research. To date, no work has been done to synthesize existing studies on depression and anxiety using Reddit. Objective The objective of this review is to understand the scope and nature of research using Reddit as a primary data source for studying depression and anxiety. Methods A scoping review was conducted using the Arksey and O’Malley framework. MEDLINE, Embase, CINAHL, PsycINFO, PsycARTICLES, Scopus, ScienceDirect, IEEE Xplore, and ACM academic databases were searched. Inclusion criteria were developed using the participants, concept, and context framework outlined by the Joanna Briggs Institute Scoping Review Methodology Group. Eligible studies featured an analytic focus on depression or anxiety and used naturalistic written expressions from Reddit users as a primary data source. Results A total of 54 studies were included in the review. Tables and corresponding analyses delineate the key methodological features, including a comparatively larger focus on depression versus anxiety, an even split of original and premade data sets, a widespread analytic focus on classifying the mental health states of Reddit users, and practical implications that often recommend new methods of professionally delivered monitoring and outreach for Reddit users. Conclusions Studies of depression and anxiety using Reddit data are currently driven by a prevailing methodology that favors a technical, solution-based orientation. Researchers interested in advancing this research area will benefit from further consideration of conceptual issues surrounding the interpretation of Reddit data with the medical model of mental health. Further efforts are also needed to locate accountability and autonomy within practice implications, suggesting new forms of engagement with Reddit users.

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Section: Resultsmentioning

confidence: 99%

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Boettcher¹

2021

JMIR Ment Health

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“…Last but not least, three writing style techniques (morphology, linguistic inquiry and word count [LIWC], and 20 factors) are also not part of stylometry, and we place them under the additional features category. Stankev et al [42] separate the morphology feature category (study of the structure and formation of words) from stylometry. On the other hand, LIWC created by Pennebaker et al [58] in 2001, uses an internal dictionary of more than 2,300 of the most common words and word stems and is categorized into linguistic and psychological categories.…”

Section: B Writing Styles In Post-internet Periodmentioning

confidence: 99%

Online Social Networks and Writing Styles–A Review of the Multidisciplinary Literature

2020

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In recent years, author identification has become an active research area, where the major differences are caused by paper or online medium, mode of entry and target audience. Much research has been devoted to analyzing writing styles in handwritten, word-processed and online social networks (OSN) texts. Word processing editors that typically include spell and grammar checkers may influence the writing style as it allows an individual to edit a piece of text to perfection. Thus, similarities may exist between OSN and word-processed texts. Moreover, none of the studies to date have made a detailed comparison of the writing styles across multidisciplinary factors. This paper attempts to close the gap between the writing styles in pre-and post-Internet periods as well as provide an in-depth comparison of the writing styles in OSN texts across three major factors: demographics, personality & behavior, and cybersecurity. The aim is to learn from past literature as we advance these techniques to OSN texts. Thus, in this paper, we also propose a novel machine learning prediction model based on tense morphology, to classify age and gender from English blogs, and the PAN 2013 dataset. This model achieves an accuracy of 94%-98% and 95%-97% for age and gender, respectively. INDEX TERMS Online social networks, survey, writing styles.

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“…This is often time-consuming and can result in smaller sample sizes. Alternatives approaches often employ data mining, either of social media sites [75] or online forums [9]. Generally, the only validation for labelling provided here is making the naive assumption that if an individual talks about certain topics, they are clinically suffering those disorders.…”

Section: Datamentioning

confidence: 99%

“…To begin with, our work demonstrates an ability to develop deep learning models to predict PHQ-4 scores given textual data using psycholinguistics feature. Compared with previous work in the area [10,75] machine learning can be used to model outputs that are easily interpretable by both machine learning and medicine domain experts. Our work is the first to propose that psychometric similarity scores might be a useful feature within this domain going forward.…”

Section: Considerations and Future Workmentioning

confidence: 99%

Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System

Delahunty¹

2019

Preprint

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Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. Current screening methods require individuals to manually complete psychometric questionnaires. In this work we develop a deep learning approach to predict psychometric scores given textual data through the use of psycholinguistics features. Data is collected via a dialogue system, were we develop and incorporate an approach to model empathy. Which aims to allow for appropriate use of these systems in a clinical setting. Following a public evaluation, we demonstrate that our approach to model empathy can out perform a similarly trained non empathic approach. Additionally, we show that our deep learning prediction approach performed well on evaluation data, but has difficulty generalizing to experimentally collected data. Limitations and implications as a result of this work are discussed.

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Feature Engineering for Depression Detection in Social Media

Cited by 49 publications

References 5 publications

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Online Social Networks and Writing Styles–A Review of the Multidisciplinary Literature

Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System

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