Psychological Well-Being of Left-Behind Children in China

Text Mining of the Social Media Website Zhihu

Dados Bibliográficos

ID15460445
AutoresYuWen Lyu (0000-0003-1562-3350, Center for Information Technology Research in the Interest of Society), Julian Chun‐chung Chow (0000-0001-6401-1284, University of California, Berkeley), Ji-Jen Hwang (0000-0003-4848-406X, Chung Yuan Christian University), Zhi Li (0000-0002-6039-1045, University of California, Berkeley), Cheng Ren (0000-0001-8717-1977, University of California, Berkeley), Xie Jun-gui (Guangzhou University, autor correspondente), Jungui Xie (Guangzhou University)
Ano2022
Volume19
Fascículo4
Páginas2127-2127
Data de publicação2022-02-14
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores do periódicoISSN: 1661-7827 • E-ISSN: 1660-4601
EditoraMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph19042127
PMID35206315
OpenAlexW4213163931
IdiomaEN
Citações recebidas6
Referências citadas46

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

Depression (economics · Linguistics · Mental health · Mindset · Narrative · Personal pronoun · Population · Psychiatry · Social media · Vocabulary · World Wide Web · Computer Science · Homelessness and Social Issues · Medicine · Mental Health via Writing · Migration, Health and Trauma · Psychology · Social Psychology · Artificial Intelligence