Social media is now being used to model mental well-being, and for understanding health
outcomes. Computer scientists are now using quantitative techniques to predict the …

Background The COVID-19 pandemic has caused several disruptions in personal and
collective lives worldwide. The uncertainties surrounding the pandemic have also led to …

Background The use of social media data to predict mental health outcomes has the
potential to allow for the continuous monitoring of mental health and well-being and provide …

Powered by machine learning techniques, social media provides an unobtrusive lens into
individual behaviors, emotions, and psychological states. Recent research has successfully …

" Human-centered machine learning"(HCML) combines human insights and domain
expertise with data-driven predictions to answer societal questions. This area's inherent …

Advances in large language models (LLMs) have empowered a variety of applications.
However, there is still a significant gap in research when it comes to understanding and …

Background. Hateful speech bears negative repercussions and is particularly damaging in
college communities. The efforts to regulate hateful speech on college campuses pose …

Emotion recognition algorithms recognize, infer, and harvest emotions using data sources
such as social media behavior, streaming service use, voice, facial expressions, and …

Stress constitutes a persistent wellbeing challenge to college students, impacting their
personal, social, and academic life. However, violent events on campuses may aggravate …

A growing body of research is combining social media data with machine learning to predict
mental health states of individuals. An implication of this research lies in informing evidence …