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.
Highlights
During China’s planned economy period, the movement of citizens was controlled, and citizens were allowed to reside only in their registered permanent residence, accordingInt
To in in this study, wewe counted fourfour types of vovocabulary associated with depression and suicide in the texts of left-behind children (LBC), namely first-person cabulary associated with depression and suicide in the texts of LBC, namely first-person singular pronouns, pronouns, negative negative words, words, past tense verbs, verbs, and and words words associated associated with with death, death, by by singular past tense text mining, and calculated the proportion of total words for each type of vocabulary
The present findings show that the proportions of four types of vocabulary in LBC personal narrative texts were all significantly higher than those in the control group, especially the proportion of first-person singular pronouns (5.37 times as high)
Summary
During China’s planned economy period, the movement of citizens was controlled, and citizens were allowed to reside only in their registered permanent residence, accordingInt. During China’s planned economy period, the movement of citizens was controlled, and citizens were allowed to reside only in their registered permanent residence, according. Res. Public Health 2022, 19, 2127 to the household registration (Hukou) system. Since the economic reform of 1978, China has worked towards a market economy and removed barriers deterring mobility, which resulted in the migration of tens of thousands of residents from rural areas to urban ones [1]
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