AUTHOR=Wang Yilin , Zhao Nan 

TITLE=Prediction model of interaction anxiousness based on Weibo data

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1045605

DOI=10.3389/fpubh.2022.1045605

ISSN=2296-2565

ABSTRACT=Adolescents who face social distress in real life are often accompanied by interaction anxiousness. In order to avoid direct social activities, they prefer to indulge in social networks to satisfy their psychological needs for interpersonal communication. Sina Weibo, China's leading social media platform, has a marked feature of young users. It provides a rich sample of adolescents with interaction anxiousness and conditions for real-time monitoring. In this study, various word categories, such as perception of spatial distance and positional relationship, morality, and emotion, showed significant relationship with interaction anxiousness. Furthermore, prediction models were established based on the original Weibo data of 839 active Sina Weibo users through a variety of machine learning algorithms, to predict the scores of users' interaction anxiousness. The results showed that the performance of the prediction model established by Fully Connected Neural Network was the best, and both criterion validity and split-half reliability were good (rcriterion validity = 0.30, rsplit-half reliability = 0.76). This study confirms the validity of the prediction model of interaction anxiousness based on social media behavior data, provides a feasible solution of examining adolescents’ interaction anxiousness, and provides scientific basis for more targeted mental health interventions.