Using Twitter to Identify Digital Phenotypes
Background:
While individuals' real-life behaviour provides valuable insights into their mental health conditions, their online behaviour has emerged as an equally informative source, serving as a "Digital Phenotype" (DP) for the identification of these conditions. The objective of the current project is to explore the association between anxiety, depression, and the DP.
Aims:
This study aims to investigate the cyber-psychological manifestations of anxiety and depression across various dimensions and patterns of social media usage and behaviour. Specifically, we will analyse selected indicators such as posting frequency, and timing, as well as conduct grammatical, lexical, and sentiment analysis of the language used in posts to identify specific phenotypes. Additionally, the project aims to validate the predictive capacity of these identified phenotypes, thereby enhancing the accuracy of disorder identification while minimizing resource expenditure.
Results can be utilized for the assessment and treatment of individuals who may be hesitant to engage with traditional mental health services to seek help.
Project significance:
The findings from this research will contribute to the development of depression and anxiety-specific profiles based on users' participation on Twitter. These profiles can be utilized for the assessment and treatment of individuals who may be hesitant to engage with traditional mental health services to seek help for their anxiety and depression symptoms.
