Psychological Well-Being of Left-Behind Children in China: Text Mining of the Social Media Website Zhihu

Yuwen Lyu, Julian Chun-Chung Chow, Ji-Jen Hwang, Zhi Li, Cheng Ren and Jungui Xie
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Yuwen Lyu: School of Economics and Statistics, Guangzhou University, Guangzhou 511400, China
Julian Chun-Chung Chow: School of Social Welfare, University of California, Berkeley, CA 94720, USA
Ji-Jen Hwang: Center for General Education, Chung Yuan Christian University, Taoyuan 320314, Taiwan
Zhi Li: School of Information, University of California, Berkeley, CA 94720, USA
Cheng Ren: School of Social Welfare, University of California, Berkeley, CA 94720, USA
Jungui Xie: School of Public Administration, Guangzhou University, Guangzhou 511400, China

IJERPH, 2022, vol. 19, issue 4, 1-13

Abstract: China’s migrant population has significantly contributed to its economic growth; however, the impact on the well-being of left-behind children (LBC) has become a serious public health problem. Text mining is an effective tool for identifying people’s mental state, and is therefore beneficial in exploring the psychological mindset of LBC. Traditional data collection methods, which use questionnaires and standardized scales, are limited by their sample sizes. In this study, we created a computational application to quantitively collect personal narrative texts posted by LBC on Zhihu, which is a Chinese question-and-answer online community website; 1475 personal narrative texts posted by LBC were gathered. We used four types of words, i.e., first-person singular pronouns, negative words, past tense verbs, and death-related words, all of which have been associated with depression and suicidal ideations in the Chinese Linguistic Inquiry Word Count (CLIWC) dictionary. We conducted vocabulary statistics on the personal narrative texts of LBC, and bilateral t -tests, with a control group, to analyze the psychological well-being of LBC. The results showed that the proportion of words related to depression and suicidal ideations in the texts of LBC was significantly higher than in the control group. The differences, with respect to the four word types (i.e., first-person singular pronouns, negative words, past tense verbs, and death-related words), were 5.37, 2.99, 2.65, and 2.00 times, respectively, suggesting that LBC are at a higher risk of depression and suicide than their counterparts. By sorting the texts of LBC, this research also found that child neglect is a main contributing factor to psychological difficulties of LBC. Furthermore, mental health problems and the risk of suicide in vulnerable groups, such as LBC, is a global public health issue, as well as an important research topic in the era of digital public health. Through a linguistic analysis, the results of this study confirmed that the experiences of left-behind children negatively impact their mental health. The present findings suggest that it is vital for the public and nonprofit sectors to establish online suicide prevention and intervention systems to improve the well-being of LBC through digital technology.

Keywords: left-behind children; personal narrative text; text mining; linguistic analysis; textual analysis; psychological well-being (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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