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Start Over Author "Liu, Xiaoqian" Publication Type Academic Journals
1,093 results on '"Liu, Xiaoqian"'

1. Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study

Author

Wang, Yameng, Ren, Xiaotong, Liu, Xiaoqian, and Zhu, Tingshao

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundAs smartphone has been widely used, understanding how depression correlates with social behavior on smartphones can be beneficial for early diagnosis of depression. An enormous amount of research relied on self-report questionnaires, which is not objective. Only recently the increased availability of rich data about human behavior in digital space has provided new perspectives for the investigation of individual differences. ObjectiveThe objective of this study was to explore depressed Chinese individuals’ social behavior in digital space through metadata collected via smartphones. MethodsA total of 120 participants were recruited to carry a smartphone with a metadata collection app (MobileSens). At the end of metadata collection, they were instructed to complete the Center for Epidemiological Studies-Depression Scale (CES-D). We then separated participants into nondepressed and depressed groups based on their scores on CES-D. From the metadata of smartphone usage, we extracted 44 features, including traditional social behaviors such as making calls and sending SMS text messages, and the usage of social apps (eg, WeChat and Sina Weibo, 2 popular social apps in China). The 2-way ANOVA (nondepressed vs depressed × male vs female) and multiple logistic regression analysis were conducted to investigate differences in social behaviors on smartphones among users. ResultsThe results found depressed users received less calls from contacts (all day: F1,116=3.995, P=.048, η2=0.033; afternoon: F1,116=5.278, P=.02, η2=0.044), and used social apps more frequently (all day: F1,116=6.801, P=.01, η2=0.055; evening: F1,116=6.902, P=.01, η2=0.056) than nondepressed ones. In the depressed group, females used Weibo more frequently than males (all day: F1,116=11.744, P=.001, η2=0.092; morning: F1,116=9.105, P=.003, η2=0.073; afternoon: F1,116=14.224, P

Published
2021
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3. Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis

Author

Wang, Yilin, Wu, Peijing, Liu, Xiaoqian, Li, Sijia, Zhu, Tingshao, and Zhao, Nan

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundDuring the COVID-19 pandemic, residential lockdowns were implemented in numerous cities in China to contain the rapid spread of the disease. Although these stringent regulations effectively slowed the spread of COVID-19, they may have posed challenges to the well-being of residents. ObjectiveThis study aims to explore the effects of residential lockdown on the subjective well-being (SWB) of individuals in China during the COVID-19 pandemic. MethodsThe sample consisted of 1790 Sina Weibo users who were residents of cities that imposed residential lockdowns, of which 1310 users (73.18%) were female, and 3580 users who were residents of cities that were not locked down (gender-matched with the 1790 lockdown residents). In both the lockdown and nonlockdown groups, we calculated SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown using individuals’ original posts on Sina Weibo. SWB was calculated via online ecological recognition, which is based on established machine learning predictive models. ResultsThe interactions of time (before the residential lockdown or after the residential lockdown) × area (lockdown or nonlockdown) in the integral analysis (N=5370) showed that after the residential lockdown, compared with the nonlockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F1,5368=13.593, P.05), while that of residents in developed areas changed. ConclusionsThese findings increase our understanding of the psychological impact and cost of residential lockdown during an epidemic. The more negative changes in the SWB of residents in developed areas imply a greater need for psychological intervention under residential lockdown in such areas.

Published
2020
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4. A Comparison of the Psycholinguistic Styles of Schizophrenia-Related Stigma and Depression-Related Stigma on Social Media: Content Analysis

Author

Li, Ang, Jiao, Dongdong, Liu, Xiaoqian, and Zhu, Tingshao

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundStigma related to schizophrenia is considered to be the primary focus of antistigma campaigns. Accurate and efficient detection of stigma toward schizophrenia in mass media is essential for the development of targeted antistigma interventions at the population level. ObjectiveThe purpose of this study was to examine the psycholinguistic characteristics of schizophrenia-related stigma on social media (ie, Sina Weibo, a Chinese microblogging website), and then to explore whether schizophrenia-related stigma can be distinguished from stigma toward other mental illnesses (ie, depression-related stigma) in terms of psycholinguistic style. MethodsA total of 19,224 schizophrenia- and 15,879 depression-related Weibo posts were collected and analyzed. First, a human-based content analysis was performed on collected posts to determine whether they reflected stigma or not. Second, by using Linguistic Inquiry and Word Count software (Simplified Chinese version), a number of psycholinguistic features were automatically extracted from each post. Third, based on selected key features, four groups of classification models were established for different purposes: (a) differentiating schizophrenia-related stigma from nonstigma, (b) differentiating a certain subcategory of schizophrenia-related stigma from other subcategories, (c) differentiating schizophrenia-related stigma from depression-related stigma, and (d) differentiating a certain subcategory of schizophrenia-related stigma from the corresponding subcategory of depression-related stigma. ResultsIn total, 26.22% of schizophrenia-related posts were labeled as stigmatizing posts. The proportion of posts indicating depression-related stigma was significantly lower than that indicating schizophrenia-related stigma (χ21=2484.64, P

Published
2020
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