Detecting Signals of Associations Between Dietary Supplement Use and Mental Disorders from Twitter

Abstract

In this study, we attempted to detect signals of association between dietary supplement use and mental disorders from Twitter. We collected tweets ranging from 2016 to 2017 which mention five dietary supplements. A case cohort of 257 users were identified by adapting a natural language processing method with further manually verified to have taken one supplement. We then randomly selected 257 users who had not taken any dietary supplement as the control cohort and compared the sentiment and mental health signals of their tweets to the case cohort. We have observed significant differences in the sad, anxious, and negative sentiment between the two cohorts. These results have shown that Twitter is a potential source for detecting signals of association between dietary supplement use and anxiety disorders, depression, and mood disorders.

A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

“…Wang et al have focused on depression, a primary reason for suicide that can be analyzed through the natural language analysis of the written text [4]. In this work, the authors have used Reddit posts to determine the parameters that indicate depressions.…”

Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text

“…15 Several studies have developed methods for the creation of DS terminology and knowledge base as well as the detection of DS-associated AEs from different data sources. [16][17][18][19][20][21][22] For example, we have extracted and standardized DS information from online sources to build an integrated DS knowledge base, (ie, iDISK). 23 And we demonstrated that as compared with the UMLS, DS terminology in the iDISK contains more novel synonyms, and achieved a better performance in a DS NER task on biomedical literature.…”

“…18 We developed methods to extract the DS usage information from Twitter, and assessed the association between DS use and mental disorders (eg, anxiety, depression). 19 We also mined AEs from DS product labels in the Dietary Supplement Label Database using topic modeling. 20 In addition, we developed a rule-based NLP system to normalize DS product names in the Dietary Supplement Label Database.…”

“…Using DS could lead to various AEs caused by individual DS use or their interactions with other concomitant DS, drugs and food due to their complicated characteristics [15]. Several studies have developed methods for the creation of DS terminology and knowledge base as well as the detection of DS associated AEs from different data sources [17][18][19][20][21][22][23]. For example, we have extracted and standardized DS information from online sources to build an integrated DS knowledge base, (i.e., iDISK) [16].…”

“…Previously, we employed signal detection methods to extract AEs associated with DS from Center for Food Safety and Applied Nutrition (CFSAN) AE reporting system (CAERS) [19]. We developed methods to extract the DS usage information from Twitter, and assessed the association between DS use and mental disorders (e.g., anxiety, depression) [20]. We also mined AEs from DS product labels in the Dietary Supplement Label Database (DSLD) using topic modeling [21].…”