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 …

Highlights•Mental illness is underdiagnosed but observable in online contexts.•
Characteristic language use patterns associated with depression have been identified and …

Depression, the most prevalent mental illness, is underdiagnosed and undertreated,
highlighting the need to extend the scope of current screening methods. Here, we use …

Depression is viewed as the largest contributor to global disability and a major reason for
suicide. It has an impact on the language usage reflected in the written text. The key …

An increasing number of works in natural language processing have addressed the effect of
bias on the predicted outcomes, introducing mitigation techniques that act on different parts …

Mental illness detection in social media can be considered a complex task, mainly due to the
complicated nature of mental disorders. In recent years, this research area has started to …

The surge in internet use to express personal thoughts and beliefs makes it increasingly
feasible for the social NLP research community to find and validate associations between …

Feature engineering plays a vital role in big data analytics. Machine learning and data
mining algorithms cannot work without data. Little can be achieved if there are few features …

Data on social media contain a wealth of user information. Big data research of social media
data may also support standard surveillance approaches and provide decision-makers with …

Users suffering from mental health conditions often turn to online resources for support,
including specialized online support communities or general communities such as Twitter …