Sensing Subjective Well-Being from Social Media

Hao, Bibo; Li, Lin; Gao, Rui; Li, Ang; Zhu, Tingshao

doi:10.1007/978-3-319-09912-5_27

Cited by 38 publications

(48 citation statements)

References 22 publications

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“…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 1 more Smart Citation

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%

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

show abstract

“…Recently, researchers have started using Twitter big data for well‐being research. This line of research focuses on deriving a computational index of subjective well‐being from social media messages as an alternative to self‐report well‐being questionnaires (Hao, Li, Gao, Li, & Zhu, ). By analyzing Tweets from 1,300 US counties, Schwartz and his colleagues (Schwartz, Eichstaedt, Kern, Dziurzynski, Lucas, Agrawal et al, ) derived Twitter topics and words that predicted life satisfaction over and above traditional predictors.…”

Section: Twitter Approach To Health Researchmentioning

confidence: 99%

Twitter Analysis: Studying US Weekly Trends in Work Stress and Emotion

Wang

Hernandez²,

Newman

et al. 2016

Applied Psychology

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We propose the use of Twitter analysis as an alternative source of data to document weekly trends in emotion and stress, and attempt to use the method to estimate the work recovery effect of weekends. On the basis of 2,102,176,189 Tweets, we apply Pennebakers linguistic inquiry word count (LIWC) approach to measure daily Tweet content across 18 months, aggregated to the US national level of analysis. We derived a word count dictionary to assess work stress and applied p-technique factor analysis to the daily word count data from 19 substantively different content areas covered by the LIWC dictionaries. Dynamic factor analysis revealed two latent factors in day-level variation of Tweet content. These two factors are: (a) a negative emotion/stress/somatic factor, and (b) a positive emotion/food/friends/home/family/leisure factor, onto which elements of work, money, achievement, and health issues have strong negative loadings. The weekly trend analysis revealed a clear "Friday dip" for work stress and negative emotion expressed on Twitter. In contrast, positive emotion Tweets showed a "mid-week dip" for Tuesday-Wednesday-Thursday and "weekend peak" for Friday through Sunday, whereas work/money/achievement/health problem Tweets showed a small "weekend dip" on Fridays through Sundays. Results partially support the Effort-Recovery theory. Implications and limitations of the method are discussed.

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“…LIWC was also used in computer science as a natural language processing tool to extract computable features from online textual data, especially in the recent boom of social media research. With the LIWC word categories as parts of the feature sets for computational prediction models, scientists could predict users' personality [18][19][20], personal values [21], tie strength [22], mental health status [23][24], subjective well-being [24][25], and even political election result [26] based on the textual data of social medias and other sources.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services

Jiao²,

et al. 2016

Self Cite

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The increasing need of automated analyzing web texts especially the short texts on Social Network Services (SNS) brings new demands of computerized text analysis instruments. The psychometric properties are the basis of the extensive use of these instruments such as the Linguistic Inquiry and Word Count (LIWC). For this study, Sina Weibo statuses were analyzed via rater coding and Simplified Chinese version of LIWC (SCLIWC), in order to evaluate the validity of SCLIWC in detecting psychological expressions in Weibo statuses (n = 60) and in identifying the psychological meaning of a single Weibo status (n = 11). Significant correlations between human ratings and SCLIWC scores and the high sensitivities of capturing single statuses with certain expressions identified by raters, proved the validity of SCLIWC in detecting psychological expressions. The results also suggested that, the efficiency of SCLIWC in detecting psychological expressions of SNS short texts could be higher if using status count scoring method, rather than the word count method as the common usage of LIWC. However, SCLIWC may not perform well in identifying the psychological meaning of a single piece of SNS short text because of its over-identification of target expressions. This study provided primary evidence of validity of SCLIWC, as well as the proper way of using it efficiently on SNS short texts.

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Sensing Subjective Well-Being from Social Media

Cited by 38 publications

References 22 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

Twitter Analysis: Studying US Weekly Trends in Work Stress and Emotion

Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services

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