Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining.

This prospective study was designed to propose a novel method of assessing proactive personality by combining text mining technology and Item Response Theory (IRT) to measure proactive personality more efficiently. We got freely expressed texts (essay question text dataset and social media text dataset) and item response data on the topic of proactive personality from 901 college students. To enhance validity and reliability, three different approaches were employed in the study. In Method 1, we used item response data to develop a proactive personality evaluation model based on IRT. In Method 2, we used freely expressed texts to develop a proactive personality evaluation model based on text mining. In Method 3, we utilized the text mining results as the prior information for the IRT estimation and built a proactive personality evaluation model combining text mining and IRT. Finally, we evaluated those three approaches via the confusion matrix indicators. The major result revealed that (1) the combined method based on essay question text, micro-blog text with pre-estimated IRT parameters performed the highest accuracy of 0.849; (2) the combined method using essay question text and pre-estimated IRT parameters performed the highest sensitivity of 0.821; (3) the text classification method based on essay question text had the best performance on the specificity of 0.959; and (4) if the models were considered comprehensively, the combined method using essay question text, micro-blog text, and pre-estimated IRT parameters achieved the best performance. Thus, we concluded that the novel combined method was significantly better than the other two traditional methods based on IRT and text mining.

Relationship Between Loneliness and Depression Among Chinese Junior High School Students: The Serial Mediating Roles of Internet Gaming Disorder, Social Network Use, and Generalized Pathological Internet Use.

This study aimed to explore the mediating effects of internet gaming disorder, social network use, and generalized pathological internet use (GPIU) on the association between loneliness and depression. A total of 2211 junior high school students completed questionnaires regarding loneliness, internet gaming disorder, social network use, GPIU, and depression (aged 10-16 years). The results of a structural equation model revealed that (a) the path coefficient of loneliness to depression was significantly positive, (b) loneliness could not predict depression through GPIU directly, but (c) loneliness could predict depression through internet gaming disorder to GPIU, (d) loneliness could predict depression through social network use to GPIU, and (e) loneliness could not predict depression through internet gaming disorder to social network use to GPIU. These results provided significant implications for the prevention and reduction of depression in Chinese junior high school students.

Relationship Between Shyness and Generalized Pathological Internet Use Among Chinese School Students: The Serial Mediating Roles of Loneliness, Depression, and Self-Esteem.

The present study aimed to explore the mediating effects of loneliness, depression, and self-esteem on the association between shyness and generalized pathological Internet use (GPIU). A total of 5215 school students completed questionnaires regarding shyness, loneliness, depression, self-esteem, and GPIU (aged 11-23 years old, M = 16.19, SD = 3.10). The self-reported scores for GPIU, shyness, loneliness, depression, and self-esteem were tested in students from elementary schools to universities. The results of a variance analysis indicated that senior high school students had the greatest prevalence of GPIU of all the study stages. With the study stages resolved, the results of a structural equation model revealed that: (a) shyness positively predicted GPIU; (b) shyness/loneliness/depression predicted GPIU through self-esteem; (c) shyness predicted GPIU through loneliness/depression → self-esteem; and (d) shyness predicted GPIU through loneliness → depression → self-esteem. In conclusion, these results provided significant implications for preventing or reducing GPIU in Chinese school students.

Relationships Between Social Support, Loneliness, and Internet Addiction in Chinese Postsecondary Students: A Longitudinal Cross-Lagged Analysis.

Using the Internet has become one of the most popular leisure activities among postsecondary students in China. Concern about the large number of students using the Internet has led to an increase in research on the influencing factors of Internet addiction and the negative consequences caused by it. This short-term longitudinal study examined the associations among three dimensions of social support [objective support (OS), subjective support (SS), and support utilization (SU)], loneliness, and the four dimensions of Internet addiction (compulsive Internet use [CIU] & withdrawal from Internet addiction [WIA], tolerance of Internet addiction [TIA], time-management problems [TMPs], and interpersonal and health problems [IHPs]) in a Chinese sample. A total of 169 postsecondary first-year students (88 girls and 81 boys; mean age = 18.31 years) participated in the study. The questionnaire measurements were taken at the beginning of the school year (T1), 6 months later (T2), and 1 year later (T3). Cross-lagged and structural equation modeling analyses indicated that (a) OS (T1) and SU (T1) negatively predicted loneliness (T2); and loneliness (T2) negatively predicted OS (T3) and SU(T3); (b) CIU & WIA (T1) and TMPs (T1) positively predicted loneliness (T2); and loneliness (T2) positively predicted CIU & WIA (T3), TIA (T3), TMP (T3), and IHP (T3); (c) SS (T1) directly affected TIA (T3) and TMP (T3); and (d) loneliness (T2) played a mediating role in the relationships between OS (T1) and CIU (T3), OS (T1) and TMP (T3), OS (T1) and IHP (T3), and SU (T1) and IHP (T3). Finally, interventions for Internet addiction and implications for future studies were discussed.