Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

Zhang, Lei; Huang, Xiaolei; Liu, Tianli; Li, Ang; Chen, Zhenxiang; Zhu, Tingshao

doi:10.1007/978-3-319-15554-8_45

Cited by 67 publications

(59 citation statements)

References 17 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%

“…It is employed to classify the polarity of a given text into categories such as positive, negative, and neutral [77]. Several studies [24,28,30,32,34,39, 49,50,52-55,57,60,65-68,70] used the well-known linguistic inquiry and word count (LIWC) [78] to extract potential signals of mental problems from textual content (eg, the word frequency of the first personal pronoun “I” or “me” or of the second personal pronoun, positive and negative emotions being used by a user or in a post). OpinionFinder [79] was used by Bollen et al [71] and SentiStrength [80] was used by Kang et al [27] and by Durahim and Coşkun [47] to carry out sentiment analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The techniques used in these studies included support vector machine (SVM) [32,33,35,38,42,56,69], linear SVM [24,27,41,46,60], and SVM with a radial basis function kernel [24,27,46,51,65-67]. Regression techniques included ridge regression [28], linear regression [37,58], log-linear regression [53,59], logistic regression [25,31,33,37,48,49,51], binary logistic regression with elastic net regularization [41,43], linear regression with stepwise selection [39,55,64], stepwise logistic regression with forward selection [50], regularized multinomial logistic regression [29], linear support vector regression [45,55], least absolute shrinkage and selection operator [55,68], and multivariate adaptive regression splines [55]. Other algorithms used for binary classification were decision trees [35,51,56,62,63], random forest [26,48,51], rules decision [62], naive Bayes [24,35,51,56,62,69], k-nearest neighbor [24,56], maximum entropy [42], neural network [69], and deep learning neural network [57].…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

Wongkoblap¹,

Vadillo²,

Ćurčin³

2017

J Med Internet Res

220

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%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Wongkoblap¹,

Vadillo²,

Ćurčin³

2017

J Med Internet Res

220

164

View full text Add to dashboard Cite

show abstract

“…Considering the effect of personality on real life [27], Bai et al has conducted research to predict the Big-Five personality of Chinese Weibo users based on user behaviors at social network sites using the decision tree method [28]. Zhang et al used linguistic features to predict suicide probability of Weibo users through linear regression [29]. Golbeck et al collected information from Twitter and made a prediction of personality using the Gaussian process [30].…”

Section: Cyber Psychometrics Methodsmentioning

confidence: 99%

A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics

et al. 2017

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Analyzing people's opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users' real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users' real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users' online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users' real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users' real mood states and could be applied in further psychological measurements and predictions.

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“…(b) To screen for posts with suicidal ideation, we trained topic models to extract linguistic features for training models. Results indicated that the models can find suicidal posts accurately (Zhang et al, 2015), which have been used to build a system for monitoring suicidal posts in real-time (http://ccpl.psych.ac.cn/suicide/).…”

mentioning

confidence: 99%

Digital Detection of Suicide Risk on Social Media

Li¹,

Zhu²

2015

Int J Emerg Ment Health

Self Cite

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Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

Cited by 67 publications

References 17 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

A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics

Digital Detection of Suicide Risk on Social Media

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