Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain.
J Med Internet Res. 2020 Dec 18;22(12):e20920. doi: 10.2196/20920.
Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for the treatment of depressive disorders. Some people share information about their experiences with antidepressants on social media platforms such as Twitter. Analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users' behavior.
This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were likely to be under SSRI treatment, in comparison to the tweets posted by the same users when they were less likely to be taking this medication.
In the first step, the timelines of Twitter users mentioning SSRI antidepressants in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning an SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before or more than 90 days after any tweet mentioning an SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study.
The number of tweets generated per day by the users when they were in treatment was higher than it was when they were in the unknown-treatment period (P=.001). When the users were in treatment, the mean percentage of tweets posted during the daytime (from 8 AM to midnight) increased in comparison to the unknown-treatment period (P=.002). The number of characters and words per tweet was higher when the users were in treatment (P=.03 and P=.02, respectively). Regarding linguistic features, the percentage of pronouns that were first-person singular was higher when users were in treatment (P=.008).
Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication. These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression.
抑郁障碍是最常见的精神疾病,也是全球致残的首要原因。选择性 5-羟色胺再摄取抑制剂(SSRIs)是治疗抑郁障碍最常用的药物。一些人会在 Twitter 等社交媒体平台上分享他们使用抗抑郁药的经验。分析接受 SSRIs 治疗的 Twitter 用户发布的信息,可以提供有关这些抗抑郁药如何影响用户行为的有用信息。
本研究旨在比较用户接受 SSRIs 治疗时和不太可能服用这种药物时发布的推文的行为和语言特征。
在第一步中,使用 128 种通用和品牌名称的 SSRIs 列表选择在推文中提到 SSRI 抗抑郁药的 Twitter 用户的时间线。在第二步中,创建了两个推文数据集,一个是治疗中数据集(由提到 SSRI 后 30 天内发布的推文组成),另一个是未知治疗数据集(由任何提到 SSRI 的推文之前或之后 90 天以上发布的推文组成)。对于每个用户,分析了这些两个数据集中分类的推文之间行为和语言特征的变化。最终包括 186 名用户及其 668842 条推文的时间线。
用户接受治疗时每天生成的推文数量高于接受未知治疗时(P=.001)。当用户接受治疗时,白天(从早上 8 点到午夜)发布的推文比例与未知治疗期相比有所增加(P=.002)。每条推文的字符和单词数在治疗时更高(分别为 P=.03 和 P=.02)。关于语言特征,当用户接受治疗时,第一人称单数代词的比例更高(P=.008)。
当患有抑郁症的用户服用抗抑郁药时,已经检测到行为和语言的变化。这些特征可以为监测这种疾病的演变提供有趣的见解,并提供与治疗依从性相关的其他信息。这些信息对于接受长期治疗(如抑郁症患者)的患者可能特别有用。