Characterization of depression in Spanish tweets: a behavioral and linguistic analysis (Preprint)

BACKGROUND Mental disorders have become a major concern in public health and 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. OBJECTIVE The aim of this study is to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users that generate them, which could suggest signs of depression. METHODS This study was developed in two steps. In the first step, the selection of users and the compilation of tweets were performed. Three datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mention that they suffer from depression), a depressive tweets dataset (a manually curated selection of tweets from the previous users that include 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 three datasets of tweets were carried out. RESULTS In comparison to 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 were the less frequent (0.4% in both cases), being this distribution different to that of the control dataset (P<.001). Sadness and anger emotions were the most common in the depressive users and depressive tweets datasets with significant differences when comparing these datasets with the control one (P<.001). As for negation words, they were detected in the 34% and 46% of the tweets in the depressive users and depressive tweets respectively, which are significantly different to the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control one (43.5%) (P<.001). CONCLUSIONS Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. Based on these changes these users can be monitored and supported, thus introducing new opportunities for the study of depression and for providing additional healthcare services to people with this disorder.