Exploring the Behavior of Users With Attention-Deficit/Hyperactivity Disorder on Twitter: Comparative Analysis of Tweet Content and User Interactions.
Exploring the Behavior of Users With Attention-Deficit/Hyperactivity Disorder on Twitter: Comparative Analysis of Tweet Content and User Interactions.
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DOI：
10.2196/43439
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发表时间：
2023-05-17
影响因子：
7.4
通讯作者：
Ferrara, Emilio
中科院分区：
文献类型：
--
作者：
Chen, Liuliu;Jeong, Jiwon;Simpkins, Bridgette;Ferrara, Emilio
关键词：
随着社交媒体的广泛使用，人们通过这些平台上的互动分享他们的实时想法和感受，包括围绕心理健康问题的互动。这可以为研究人员提供一个新的机会来收集与健康相关的数据来研究和分析精神障碍。然而，作为最常见的精神障碍之一，关于注意力缺陷多动障碍（ADHD）在社交媒体上表现的研究却很少。本研究旨在通过Twitter上的文本内容和元数据来研究和识别ADHD用户在Twitter上的不同行为模式和互动。首先，我们建立了2个数据集：一个ADHD用户数据集包含3135名在Twitter上明确报告患有ADHD的用户，另一个控制数据集由3223名随机选择的没有ADHD的Twitter用户组成。收集两个数据集中用户的所有历史tweets。本研究采用混合方法。我们通过Top2Vec主题建模提取ADHD用户和非ADHD用户频繁提及的话题，并通过主题分析进一步比较两组在这些话题下讨论内容的差异。我们使用一个蒸馏伯特情绪分析模型来计算情绪类别的情绪得分，并比较情绪强度和频率。最后，我们从推文的元数据中提取用户的发布时间、推文类别以及关注者和关注者数量，并比较这些特征在ADHD组和非ADHD组之间的统计分布。与非ADHD数据集的对照组相比，患有ADHD的用户在推特上表示无法集中注意力、管理时间、睡眠障碍和滥用药物。患有ADHD的用户更频繁地感到困惑和烦恼，而他们感到的兴奋、关心和好奇心更少（均P< 0.001）。ADHD患者对情绪更敏感，感觉更紧张、悲伤、困惑、愤怒和娱乐（均P< 0.001）。在推特发布特征方面，与对照组相比，ADHD用户在推特发布方面更为活跃（P= 0.04），尤其是在午夜至早上6点之间（P< 0.001）；原创内容推文数量增加（P< 0.001）；在Twitter上关注的人更少（P< 0.001）。这项研究揭示了与没有多动症的人相比，患有多动症的用户在Twitter上的行为和互动是如何不同的。基于这些差异，研究人员、精神科医生和临床医生可以将Twitter作为一个潜在的强大平台来监测和研究ADHD患者，为他们提供额外的医疗支持，改进ADHD的诊断标准，并设计辅助工具来自动检测ADHD。
With the widespread use of social media, people share their real-time thoughts and feelings via interactions on these platforms, including those revolving around mental health problems. This can provide a new opportunity for researchers to collect health-related data to study and analyze mental disorders. However, as one of the most common mental disorders, there are few studies regarding the manifestations of attention-deficit/hyperactivity disorder (ADHD) on social media. This study aims to examine and identify the different behavioral patterns and interactions of users with ADHD on Twitter through the text content and metadata of their posted tweets. First, we built 2 data sets: an ADHD user data set containing 3135 users who explicitly reported having ADHD on Twitter and a control data set made up of 3223 randomly selected Twitter users without ADHD. All historical tweets of users in both data sets were collected. We applied mixed methods in this study. We performed Top2Vec topic modeling to extract topics frequently mentioned by users with ADHD and those without ADHD and used thematic analysis to further compare the differences in contents that were discussed by the 2 groups under these topics. We used a distillBERT sentiment analysis model to calculate the sentiment scores for the emotion categories and compared the sentiment intensity and frequency. Finally, we extracted users’ posting time, tweet categories, and the number of followers and followings from the metadata of tweets and compared the statistical distribution of these features between ADHD and non-ADHD groups. In contrast to the control group of the non-ADHD data set, users with ADHD tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. Users with ADHD felt confusion and annoyance more frequently, while they felt less excitement, caring, and curiosity (all P<.001). Users with ADHD were more sensitive to emotions and felt more intense feelings of nervousness, sadness, confusion, anger, and amusement (all P<.001). As for the posting characteristics, compared with controls, users with ADHD were more active in posting tweets (P=.04), especially at night between midnight and 6 AM (P<.001); posting more tweets with original content (P<.001); and following fewer people on Twitter (P<.001). This study revealed how users with ADHD behave and interact differently on Twitter compared with those without ADHD. On the basis of these differences, researchers, psychiatrists, and clinicians can use Twitter as a potentially powerful platform to monitor and study people with ADHD, provide additional health care support to them, improve the diagnostic criteria of ADHD, and design complementary tools for automatic ADHD detection.
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DOI：
10.1073/pnas.1702247114
发表时间：
2017-09-19
影响因子：
11.1
作者：
Cowen, Alan S.;Keltner, Dacher
通讯作者：
Keltner, Dacher
影响因子：
25.4
作者：
Cowen, Alan;Sauter, Disa;Keltner, Dacher
通讯作者：
Keltner, Dacher
影响因子：
16.4
作者：
Cowen, Alan S.;Keltner, Dacher
通讯作者：
Keltner, Dacher
影响因子：
29.9
作者：
Cowen, Alan S.;Laukka, Petri;Keltner, Dacher
通讯作者：
Keltner, Dacher
影响因子：
16.4
作者：
Cowen, Alan S.;Elfenbein, Hillary Anger;Keltner, Dacher
通讯作者：
Keltner, Dacher