Time Series Analysis for Social Media Anxiety Detection: A Mini Review

According to the World Health Organization, mental health refers to the good psychological well-being of a person. Due to genetical, environmental, biological, and psychological reasons an individual’s capability to cope with day-to-day work may reduce resulting in various mental issues like anxiety, depression, and bipolar disorder. With the rapid growth of social-media penetration, people tend to share their real-life updates. Social media data is a good source which reflects one’s mood, tension, and feelings. These psychological behaviours could vary over time, indicating the importance of the time factor. Monitoring a person’s post behaviour allows more dynamic and contextual understanding of a person’s mental state beyond static assessments. This paper provides a narrative review of social media anxiety detection using time series analysis over the last decade. Here we answer the research question on, what are the existing methods, challenges, and future directions on social media time series anxiety detection.