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Microblog discourse analysis for parenting style assessment
Zihan Wei1, Lei Cao2, Zhihong Qiao2
1School of Economics and Management, Beijing University of Chemical Technology, Beijing, China.
Negative parenting styles contribute to student mental health issues. This study uses microblog analysis to assess parenting styles, offering a novel tool for early intervention and suicide prevention among university students.
Area of Science:
- Computational Psychology
- Natural Language Processing
- Mental Health Informatics
Background:
- Negative parenting styles are linked to increased anxiety, depression, and suicide risk in university students.
- Social media platforms, specifically microblogs, present an opportunity for non-invasive assessment of parental influence on students.
Purpose of the Study:
- To investigate the relationship between university students' microblog content and their parents' parenting styles.
- To develop a predictive model for assessing parenting styles based on student microblogging activity.
Main Methods:
- Analysis of 111,258 microblog posts from 575 university students using frequency analysis of topical and emotional words.
- Development of a novel parenting style assessment method incorporating a correlation injection module.
Main Results:
- The developed method demonstrated superior performance compared to baseline Natural Language Processing (NLP) techniques, including ChatGPT-4.
- Mean Squared Error (MSE) was reduced by 14% to 0.12, indicating effective assessment accuracy.
Conclusions:
- This research introduces a pioneering microblog-based tool for assessing parenting styles, merging psychological insights with computational methods.
- The tool can aid healthcare institutions in identifying at-risk students, facilitating timely interventions for suicide prevention and enhancing overall well-being.
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