Sharma, TPanchendrarajan, RSaxena, A2022-11-292022-11-292022-09-19Sharma, T., Panchendrarajan, R., Saxena, A. (2022). Characterisation of Mental Health Conditions in Social Media Using Deep Learning Techniques. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_9978-3-031-10868-6https://rda.sliit.lk/handle/123456789/3084Social media has become the easiest and most popular choice for online users to share their views, thoughts, opinions, and emotions with their friends and followers. The shared content is very helpful in making valuable conclusions about an individual’s personality. Nowadays, researchers across the world are collecting social media data for understanding the mental health of social media users. Mental illness is a big concern in today’s society, and in no time, it can turn into suicidal thoughts if efficacious methods are not taken into account. Early detection of such mental health conditions provides a potential way for effective social intervention. This has opened up opportunities to the research community to automatically determine various mental health conditions, such as anxiety, depression, and near-suicidal thoughts from user-generated content. The deep learning techniques have been extensively used in this research area to better understand the mental health of users on social networking platforms. In this chapter, we conduct a comprehensive review of the research works that use deep learning techniques to identify various mental health conditions from social media data. We discuss methods to detect different types of mental health conditions, such as anxiety, depression, stress, suicide, and anorexia. We also provide the details of the datasets used in these studies. The chapter is concluded with promising future directions.enCharacterisationMental Health ConditionsSocial MediaUsing Deep LearningTechniquesArticlehttps://doi.org/10.1007/978-3-031-10869-3_9