Unveiling Mental Health Signals on Social Media Using Functional Data Techniques

Music City Center 

The COVID-19 pandemic has precipitated a significant mental health crisis, notably impacting young adults. With the rapid advancement of technology and the ubiquitous use of social media among this demographic, there has been a marked increase in the expression of emotions and mental health concerns on these platforms. We propose methods to study trends in mental health issues, self-harm, and violence towards others as conveyed through social media posts. Traditional methods of analyzing such data often lack interpretability, high computational demands, or inflexibility in data handling. In this work, we develop a flexible statistical framework designed to analyze high-resolution data from social media, aiming to identify users exhibiting atypical posting behaviors.