On estimating depressive tendencies of Twitter users utilizing their tweet data

Tsugawa, Sho; Yukiko, Mogi; Kikuchi, Yusuke; Kishino, Fumio; Fujita, Kazuyuki; Itoh, Yoshio; Ohsaki, Hiroyuki

doi:10.1109/vr.2013.6549431

Cited by 25 publications

(31 citation statements)

References 5 publications

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“…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%

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

Wongkoblap¹,

Vadillo²,

Ćurčin³

2017

J Med Internet Res

221

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

221

164

View full text Add to dashboard Cite

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“…In Park et al [28], a similar analysis is performed by analyzing data from Facebook. Using multiple regression analysis, Tsugawa et al [37] show that frequencies of word usage on Twitter are useful features for recognizing depression among users. The main objective of such research is to clarify which features that can obtained from user activity are useful for estimating the severity of depression.…”

Section: Related Workmentioning

confidence: 99%

“…Tsugawa et al showed that the word frequencies are useful for identifying depression [37]. MeCab [20] was used to for morphological stemming and categorization of the Japanese tweet text to obtain accurate word frequencies.…”

Section: Features For Recognizing Depressionmentioning

confidence: 99%

“…For example, Tsugawa et al [37] found that frequency of word usage in messages (hereinafter, tweets) on the popular microblogging service Twitter could be used for recognizing depression among users. Pioneering work by De Choudhury et al [14] describes how to construct support vector machine (SVM) classifiers that can be used to identify users with depression using features obtained from the records of individual users' activities on Twitter.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recognizing Depression from Twitter Activity

Tsugawa

Kikuchi

Kishino

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Self Cite

266

235

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In this paper, we extensively evaluate the effectiveness of using a user's social media activities for estimating degree of depression. As ground truth data, we use the results of a web-based questionnaire for measuring degree of depression of Twitter users. We extract several features from the activity histories of Twitter users. By leveraging these features, we construct models for estimating the presence of active depression. Through experiments, we show that (1) features obtained from user activities can be used to predict depression of users with an accuracy of 69%, (2) topics of tweets estimated with a topic model are useful features, (3) approximately two months of observation data are necessary for recognizing depression, and longer observation periods do not contribute to improving the accuracy of estimation for current depression; sometimes, longer periods worsen the accuracy.

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“…As far as Twitter is concerned it has also been shown that tweets can give indications of depression [8,42]. Following features used as part of depression classifiers, we decided to use certain categories of the Linguistic Inquire and Word Count (LIWC) dictionary 9 [31].…”

Section: Evidence For Post-breakup Depressionmentioning

confidence: 99%

From “I Love You Babe” to “Leave Me Alone” - Romantic Relationship Breakups on Twitter

Garimella

Weber²,

Cin

2014

Lecture Notes in Computer Science

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Abstract. We use public data from Twitter to study the breakups of the romantic relationships of 661 couples. Couples are identified through profile references such as @user1 writing "@user2 is the best boyfriend ever!!". Using this data set we find evidence for a number of existing hypotheses describing psychological processes including (i) pre-relationship closeness being indicative of post-relationship closeness, (ii) "stonewalling", i.e., ignoring messages by a partner, being indicative of a pending breakup, and (iii) post-breakup depression. We also observe a previously undocumented phenomenon of "batch un-friending and being un-friended" where users who break up experience sudden drops of 15-20 followers and friends. Our work shows that public Twitter data can be used to gain new insights into psychological processes surrounding relationship dissolutions, something that most people go through at least once in their lifetime.

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On estimating depressive tendencies of Twitter users utilizing their tweet data

Cited by 25 publications

References 5 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

Recognizing Depression from Twitter Activity

From “I Love You Babe” to “Leave Me Alone” - Romantic Relationship Breakups on Twitter

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