基于社交文本的高中到大学过渡阶段青少年情绪研究

  目的  分析大学生在社交网络平台上的文本信息，了解其在高中到大学过渡阶段时情绪状态及变化轨迹，为开展青少年心理健康教育提供参考。  方法  于2018年9—11月及2019年9—10月，通过线上和线下方式招募南昌大学534名本科生，提取其高三到大一时间区间内发布的QQ说说，以“月”为时间颗粒度进行标记。使用结巴分词程序和情绪词典，计算每月说说总词数、条数、积极情绪和消极情绪词平均词频。描述高中到大学阶段QQ说说的基本活跃度，使用多层线性模型分析消极情绪和积极情绪词词频的月变化趋势，进一步对高中和大学两阶段的基本活跃度和月变化趋势进行比较。  结果  青少年在进入大一后，对社交平台的使用度较高，说说总词数、条数、积极情绪、消极情绪词数均高于高三阶段(Wilcoxon Z值分别为-18.93，-18.03，-18.40，-13.60，P值均＜0.01)；大一阶段，女生发表说说的说说总词数、条数、积极情绪、消极情绪词数均高于男生(Mann-Whitney U值分别为26 499.00，28 581.00，24 636.00，28 010.50，P值均＜0.05)。青少年说说积极情绪词比消极情绪词更多，积极情绪词的月平均词频(0.029)是消极情绪词(0.012)的2.40倍；在高三阶段的积极情绪词频、消极情绪词频会随着月份发生显著降低(β值分别为-6.01，-3.55；t值分别为-3.40，-2.86，P值均＜0.01)。  结论  青少年在高三阶段使用QQ说说的活跃度较低，但其说说中体现出的积极情绪、消极情绪波动均更为剧烈，高三阶段学生的情绪状态值得关注。

Adolescent emotion during the transition stage from high school to college based on social network platform

  Objective  To understand the emotion state and emotion trajectory of adolescents during the transition stage from high school to college by analyzing text on social network platform, and to provide reference for carring out mental education for adolescents.  Methods  From September to November 2018 and September to October 2019, 534 undergraduates were recruited through online and offline advertisement, and their Tencent Tweets posted during the period from last year of high school to first year of college were crawled and stored with the time stamps. Total number of words, the number of Tencent Tweets, frequency of positive emotion words and negative emotion words were obtained by using jieba (a word segmentation program) and an emotion dictionary. The degree of activity in Tencent Tweets during the transition stage were described. The trend of the frequency of negative and positive emotion words were analyzed by using hierarchical linear model. The degree of activity and trend of monthly variation between high school and colleges were also compared.  Results  After entering college, adolescents' degree of activity in Tencent Tweets were much higher. Total number of words, number of Tencent Tweets, number of positive emotion words and negative emotion words were significantly higher than those in high school (Wilcoxon Z=-18.93, -18.03, -18.40, -13.60, P < 0.01). Total number of words, number of Tencent Tweets, number of positive emotion words and negative emotion words posted by female adolescents were significantly higher than male adolescents during first year of college (Mann-Whitney U=26 499.00, 28 581.00, 24 636.00, 28 010.50, P < 0.05). There were more positive emotion words than negative emotion words in adolescents' Tencent Tweets, the monthly average frequency of positive emotion words (0.029) was 2.40 times of frequency of negative emotion words (0.012). The frequency of positive emotion words and negative emotion words decreased significantly with each month in the last year of high school (β=-6.01, 3.55; t=-3.40, -2.86, P < 0.01).  Conclusion  Adolescents were less active in using Tencent Tweets in last year of high school, but their positive and negative emotional fluctuation were more intense in Tencent Tweets. It is noteworthy to pay attention to the emotional state of adolescents in last year of high school.