Leis Angela, Ronzano Francesco, Mayer Miguel A, Furlong Laura I, Sanz Ferran
Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
J Med Internet Res. 2019 Jun 27;21(6):e14199. doi: 10.2196/14199.
Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English.
The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression.
This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out.
In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001).
Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.
精神障碍已成为公共卫生领域的主要关注点，是全球疾病总负担的主要原因之一。社交媒体平台使我们能够观察人们日常生活中的活动、思想和感受，包括患有精神障碍的患者。已有研究分析了包括抑郁症在内的精神障碍对社交媒体用户行为的影响，但这些研究通常集中在英文撰写的信息上。
本研究旨在识别西班牙语推文中的语言特征以及发布推文的推特用户的行为模式，这些特征可能暗示抑郁迹象。
本研究分两步进行。第一步，进行用户选择和推文汇编。共创建了3个推文数据集，一个抑郁用户数据集（由90名明确表示患有抑郁症的用户的时间线组成），一个抑郁推文数据集（从先前用户的推文中手动挑选，包括表明抑郁的表达），以及一个对照数据集（由450名随机选择的用户的时间线组成）。第二步，对3个推文数据集进行比较和分析。
与对照数据集相比，抑郁用户发布推文的活跃度较低，在23:00至6:00之间发布更为频繁（P<0.001）。对照数据集使用名词的百分比几乎是抑郁用户的两倍（P<0.001）。相比之下，动词在抑郁用户数据集中的使用更为常见（P<0.001）。第一人称单数代词在抑郁用户数据集中的使用最为频繁（80%），第一和第二人称复数代词使用最少（两者均为0.4%），这种分布与对照数据集不同（P<0.001）。与悲伤、愤怒和厌恶相关的情绪在抑郁用户和抑郁推文数据集中更为常见，将这些数据集与对照数据集进行比较时存在显著差异（P<0.001）。至于否定词，在抑郁用户和抑郁推文中分别有34%和46%的推文中检测到，这与对照数据集有显著差异（P<0.001）。抑郁用户（54%）和抑郁推文（65%）数据集中的负极性比对照数据集（43.5%）更频繁（P<0.001）。
可能患有抑郁症的推特用户会改变其语言的一般特征以及他们在社交媒体上的互动方式。基于这些变化，可以对这些用户进行监测和支持，从而为研究抑郁症带来新的机会，并为患有这种疾病的人提供额外的医疗服务。
