@文章{信息:doi/10.2196/20920，作者=“Leis, Angela和Ronzano, Francesco和Mayer, Miguel Angel和Furlong, Laura I和Sanz, Ferran”，标题=“评估抑郁症药物治疗期间的行为和语言变化使用西班牙语推文:对比较研究”，期刊=“J医学互联网Res”，年=“2020”，月=“12月”，日=“18”，卷=“22”，数字=“12”，页=“e20920”，关键词=“抑郁症;抗抑郁药物;5 -羟色胺吸收抑制剂;心理健康;社交媒体;infodemiology;背景:抑郁症是最常见的精神疾病，也是全球致残的主要原因。选择性血清素再摄取抑制剂(SSRIs)是治疗抑郁症最常用的处方药。有些人在推特等社交媒体平台上分享他们服用抗抑郁药的经历。对接受SSRI治疗的Twitter用户发布的消息进行分析，可以得出这些抗抑郁药物如何影响用户行为的有用信息。 Objective: 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. Methods: 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. Results: 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). Conclusions: 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. ", issn="1438-8871", doi="10.2196/20920", url="//www.mybigtv.com/2020/12/e20920/", url="https://doi.org/10.2196/20920", url="http://www.ncbi.nlm.nih.gov/pubmed/33337338" }