Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users’ social media data with SWB labels, we train machine learning models that are able to “sense” individual SWB. Our model, which attains the state-of-the-art prediction accuracy, can then be applied to identify large amount of social media users’ SWB in time with low cost.
Kosinskia @cite_3 , Schwartz @ analyzed the correlation between users' personal traits and behaviors or language usage on Facebook. in which users' personality traits are measured by using Big-Five Personality Inventory. They also build models to predict users' traits like personality through social media, which is also another evidence of convergent validation'' approach. @cite_4 use 839 behavioral features on microblog to predict personality, which proved the feasibility of predicting psychological variables through behavioral features. Similar works @cite_12 are also conducted on social media like Twitter. Studies have also cast interests to the prediction of mental health status via social media. Hao @cite_1 , Choudhury @ generalize this method to prediction depression, anxiety, etc. analyzed users' both behavioral and linguistic features on microblog, and employ machine learning methods to predict depression, anxiety and other mental health status of individuals.
@cite_1 @cite_4 @cite_12 @cite_3
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Psychology research suggests that certain personality traits correlate with linguistic behavior. This correlation can be effectively modeled with statistical natural language processing techniques. Prediction accuracy generally improves with larger data samples, which also allows for more lexical features. Most existing work on personality prediction, however, focuses on small samples and closed-vocabulary investigations. Both factors limit the generality and statistical power of the results. In this paper, we explore the use of social media as a resource for large-scale, openvocabulary personality detection. We analyze which features are predictive of which personality traits, and present a novel corpus of 1.2M English tweets annotated with Myers-Briggs personality type and gender. Our experiments show that social media data can provide sufficient linguistic evidence to reliably predict two of four personality dimensions. The rapid development of social media brings about vast user generated content. Computational cyber-psychology, an interdisciplinary subject area, employs machine learning approaches to explore underlying psychological patterns. Our research aims at identifying users’ mental health status through their social media behavior. We collected both users’ social media data and mental health data from the most popular Chinses microblog service provider, Sina Weibo. By extracting linguistic and behavior features, and applying machine learning algorithms, we made preliminary exploration to identify users’ mental health status automaticly, which previously is mainly measured by well-designed psychological questionnaire. Our classification model achieves the accuracy of 72 , and the continous predicting model achieved correlation of 0.3 with questionnaire based score. Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84 to 92 . We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors. Social media sites are now the most popular destination for Internet users, providing social scientists with a great opportunity to understand online behaviour. There are a growing number of research papers related to social media, a small number of which focus on personality prediction. To date, studies have typically focused on the Big Five traits of personality, but one area which is relatively unexplored is that of the anti-social traits of narcissism, Machiavellians and psychopathy, commonly referred to as the Dark Triad. This study explored the extent to which it is possible to determine anti-social personality traits based on Twitter use. This was performed by comparing the Dark Triad and Big Five personality traits of 2,927 Twitter users with their profile attributes and use of language. Analysis shows that there are some statistically significant relationships between these variables. Through the use of crowd sourced machine learning algorithms, we show that machine learning provides useful prediction rates, but is imperfect in predicting an individual's Dark Triad traits from Twitter activity. While predictive models may be unsuitable for predicting an individual's personality, they may still be of practical importance when models are applied to large groups of people, such as gaining the ability to see whether anti-social traits are increasing or decreasing over a population. Our results raise important questions related to the unregulated use of social media analysis for screening purposes. It is important that the practical and ethical implications of drawing conclusions about personal information embedded in social media sites are better understood.