Citation

Liu RX, Liu H. Front. Psychol. 2022; 13: 785639.

Copyright

(Copyright © 2022, Frontiers Research Foundation)

DOI

10.3389/fpsyg.2022.785639

PMID

35360618

PMCID

PMC8962829

Abstract

Emotional stability is of great importance for undergraduates and has significant predictive power for mental health. Emotions are associated with individuals' daily lives and routines. Undergraduates commonly post their opinions and feelings on social networks, providing a huge amount of data for studying their emotional states and rhythms. Based on the construction of the emotion dictionary of undergraduates' Tencent tweets (TTs)-a social network for users to share their life situations and express emotions and feelings to friends-we used big data text analysis technology to analyze the emotion words in 45,996 Tencent tweets published by 894 undergraduates. Then, we used hierarchical linear modeling to further analyze the daily rhythms of undergraduate students' emotions and how demographic variables are associated with the daily rhythmic changes. The results were as follows: (1) Undergraduates tweeted about more positive emotions than negative emotions (love was most common and fear was the least common); (2) The emotions in undergraduates' tweets changed considerably from 1 a.m. to 6 a.m., but were fairly stable during the day; (3) There was a rising trend in the frequency of using emotion words in Tencent tweets during the day as each hour progressed, and there was a higher increase in positive emotion than negative emotion; and (4) The word frequencies and daily rhythms of emotions varied depending on demographic variables. Gender was correlated with the frequencies of gratitude and the daily rhythms of anger. As the grade increased, the frequency of emotion words in most subcategories in TTs decreased and the fluctuation in daily rhythms became smaller. There was no significant difference in the frequency and daily rhythm of emotion words used in TTs based on having had a left-behind experience. The results of the present study provided emotion expression in social networks in Chinese collectivist culture. This study added new evidence to support the notion that positive and negative emotions are independent dimensions.

Language: en

Keywords

daily rhythm; emotion; Tencent tweets (TTs); text analysis; undergraduate student