Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

Braithwaite, Scott R.; Giraud‐Carrier, Christophe; West, Josh; Barnes, Michael; Hanson, Carl L.

doi:10.2196/mental.4822

Cited by 171 publications

(108 citation statements)

References 53 publications

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“…The instruments used to detect suicidal ideation and the possibility of an individual committing suicide were the Suicide Probability Scale [32,39,48], the Acquired Capability for Suicide Scale [30], and the Interpersonal Needs Questionnaire [30]. Satisfaction with life and well-being were measured with the Satisfaction with Life Scale [28,34], the Positive and Negative Affect Schedule [55], and the Psychological Well-Being Scale [55].…”

Section: Resultsmentioning

confidence: 99%

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

Wongkoblap¹,

Vadillo²,

Ćurčin³

2017

J Med Internet Res

230

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

230

164

View full text Add to dashboard Cite

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“…Machine learning approaches have been used to predict health related issues and behaviors through social media [25,47], electronic health records [48,49], and accelerometer data [50]. As illustrated through the application of a pruned decision tree analysis (see Figure 2) of Twitter data, the model capably predicts ED with 91% accuracy among identified followers of …”

Section: Discussionmentioning

confidence: 99%

An Exploration of Eating Disorders and Emotional States: An Infodemiological Study of Tweets Among Followers of Pro-Eating Disorder Sites

Whitney¹,

Hovey²,

Partington³

et al. 2017

Preprint

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“…In the same line of research, O'Dea et al [18] confirmed that Twitter is used by individuals to express suicidality and demonstrated that it is possible to distinguish the level of concern among suiciderelated tweets, using both human coders and an automatic machine classifier. These insights have also been investigated by Braithwaite et al [19] who demonstrated that machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. For a more detailed review of the use of social media platforms as a tool for suicide prevention, the reader may refer to the recent systematic survey by Robinson et al [20].…”

Section: Literature Reviewmentioning

confidence: 93%

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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Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

Cited by 171 publications

References 53 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

An Exploration of Eating Disorders and Emotional States: An Infodemiological Study of Tweets Among Followers of Pro-Eating Disorder Sites

Detection of suicide-related posts in Twitter data streams

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