Detecting suicidality on Twitter

O’Dea, Bridianne; Wan, Stephen; Batterham, Philip J.; Calear, Alison L.; Paris, Cécile; Christensen, Helen

doi:10.1016/j.invent.2015.03.005

Cited by 349 publications

(236 citation statements)

References 19 publications

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“…Emoticons were converted to other forms such as ASCII codes [45] to ensure data were machine readable. Anonymization was also performed to remove any potentially identifiable usernames [31,33,35,52,53,70]. …”

Section: Resultsmentioning

confidence: 99%

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Wongkoblap¹,

Vadillo²,

Ćurčin³

2017

J Med Internet Res

208

164

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BackgroundMental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.ObjectiveThe objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.MethodsWe performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.ResultsThe initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.ConclusionsDespite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

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Section: Resultsmentioning

confidence: 99%

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Wongkoblap¹,

Vadillo²,

Ćurčin³

2017

J Med Internet Res

208

164

View full text Add to dashboard Cite

show abstract

“…Nguyen et al (2014) separate out LiveJournal posts that discuss depression and related topics. Homan et al (2014) and O'Dea et al (2015) detect posts containing suicide ideation and distress, and Li et al (2015) investigate unhelpful, stigmatizing reactions to suicide on the Chinese social media platform Weibo. Milne et al (2016) host a shared task for identifying and prioritizing concerning content on ReachOut.com's peer support forum.…”

Section: Nlp In Mental Health Applications 651 2 Methods and Overviewmentioning

confidence: 99%

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

Self Cite

212

148

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Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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“…Nevertheless, it is an approach consistent with Twitter's own diversity of use and provides a qualitative baseline for further analysis. 15 There has been much written about the difficulties of interpreting the emotional content of social media text (Thelwall and Kappas, 2014) including statements of suicidality (O'Dea et al, 2015). Approaches such as sentiment analysis are error-prone and Twitter data are seen as peculiarly 'noisy' (Kim et al, 2012).…”

Section: Methodology: Following Feelings Onlinementioning

confidence: 99%

Empathy Rituals: Small Conversations about Emotional Distress on Twitter

Brownlie

Shaw

2018

Sociology

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There is growing research interest in the sharing of emotions through social media. Usually centred on 'newsworthy' events and collective 'flows' of emotion, this work is often computationally driven. This article presents an interaction-led analysis of small data from Twitter to illustrate how this kind of intensive focus can 'thicken' claims about emotions, and particularly empathy. Drawing on Goffman's work on ritual, we introduce and then apply the idea of 'empathy rituals' to exchanges about emotional distress on Twitter, a platform primarily researched using big data approaches. While the potential of Goffman's work has been explored in some depth in relation to digital performances, its emotional dimension has been less fully examined. Through a focus on Twitter conversations, we show how reading small data can inform computational social science claims about emotions and add to sociological understanding of emotion in (digital) publics.

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Detecting suicidality on Twitter

Cited by 349 publications

References 19 publications

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Natural language processing in mental health applications using non-clinical texts

Empathy Rituals: Small Conversations about Emotional Distress on Twitter

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