论著

微博语境下ADHD特征群体的情感表达模式研究

  • 刘星宇 ,
  • 余萌
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  • 1南方医科大学公共卫生学院心理学系,广州 510515;
    2南方医科大学珠江医院精神心理科,广州 510282
刘星宇,南方医科大学2025级应用心理学专业在读硕士。研究方向为儿童青少年社交焦虑。
* 余萌 yumengbnu@163.com

网络出版日期: 2025-12-10

基金资助

广州市基础与应用基础研究青年博士“起航”项目(2024A04J4515); 广东省哲学社会科学规划2022年度青年项目(GD22YXL04)

Emotional Expression Patterns in Weibo Posts by Individuals with ADHD Traits

  • LIU Xingyu ,
  • YU Meng
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  • 1Department of Psychology, School of Public Health, Southern Medical University, Guangzhou 510515, China;
    2Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
* YU Meng, yumengbnu@163.com

Online published: 2025-12-10

摘要

目的:基于微博文本分析,探究注意缺陷多动障碍(attention deficit/hyperactive disorder,ADHD)特征群体在微博语境下的情感特征与表达模式。
方法:使用网络爬虫提取微博2022年4月1日至2025年4月1日期间ADHD相关博文,采用R软件进行文本清洗后,按照特定的标准人工筛选ADHD特征群体以第一人称视角发表的带有情感表达的文本,最终纳入207条ADHD相关文本(包含1β116条情感语句)。采用大连理工大学情感词典进行情感分类,结合Jaccard相似度系数(Jaccard similarity coefficient,J)与马尔可夫链探究ADHD特征群体的情感共现与动态转移特点。
结果:(1)矛盾情感高频共现(如“赞扬-贬责”,J=0.58),既反映了日常自我评价的冲突,也揭示了更深层次的自我认同矛盾;(2)统计发现情感特征存在高概率转移路径(如烦闷→贬责转移概率为90.9%)。
结论:ADHD特征群体在社交媒体呈现独特的矛盾性情感表达特征,其情感动态特征可为数字化干预提供精准靶点。

本文引用格式

刘星宇 , 余萌 . 微博语境下ADHD特征群体的情感表达模式研究[J]. 心理学通讯, 2025 , 8(3) : 233 -241 . DOI: 10.12100/j.issn.2096-5494.225032

Abstract

Objective: This study employs Weibo text analysis to investigate the emotional characteristics and expression patterns of individuals with attention deficit/hyperactivity disorder (ADHD) traits within the context of Weibo posts.
Methods: ADHD-related posts were collected from Weibo from 2022 to 2025 using a web crawler. Following data cleaning with R, manual screening was conducted based on specific criteria to identify: (1) posts from suspected ADHD individuals, and (2) first-person narratives containing emotional expressions. A final dataset of 207 eligible posts (containing 1 116 emotional clauses) was analyzed. Emotional categorization was performed using the Dalian University of Technology Sentiment Lexicon. The Jaccard coefficient and Markov chain analysis were applied to examine the patterns of emotional co-occurrence and dynamic transitions.
Results: The analysis revealed: (1) frequent co-occurrence of contradictory emotions (e.g., praise-blame, Jaccard coefficient=0.58), reflecting conflicts in daily self-evaluation and indicating deeper self-identity issues; (2) statistics reveal high-probability emotional transition pathways (e.g., annoyance → blame, 90.9%).
Conclusion: Individuals with suspected ADHD demonstrate a unique profile of contradictory emotional expression on social media. The characteristics of their emotional dynamics may provide precise targets for digital health interventions.

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