Abstract
Online social networking has caused profound changes within the manner folks communicate and move. These changes might have an effect on sure traditional aspects of human behavior and cause medical specialty disorders. Mental disease is quickly turning into one in every of the foremost current public unfitness around the world. Social media networks, wherever clients will categorical their emotions, feelings, and thoughts, area unit a worthy supply of knowledge for analyzing mental state, and procedures supported machine intelligence area unit more and more used for this purpose. It is difficult to sight social network mental disorder (SNMDs) as a result of the mental factors thought of in existing diagnostic criteria (questionnaire) cannot be determined from online group action logs. To mechanically sight SNMDs cases of OSN clients, taking out these constraints to evaluate user’s online psychological states is extremely difficult. As an instance, the range of isolation with the impact of less inhibition of OSN clients do not seem to be simply discernible. Abnormal activity connected keywords area unit generated and hold on server. Each user activity (tweets, post, comments, etc.) information area unit hold on in information that may be accustomed analyze folie. This can facilitate to observe user activities in social network. Projected work detects sever style of SNMDs with a binary SVM classification approach.
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Reddy, B.D., Joshua, E.S.N., Rao, N.T., Bhattacharyya, D. (2023). Mental Disorder Detection in Social Networks Using SVM Classification: An Improvised Approach. In: Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 558. Springer, Singapore. https://doi.org/10.1007/978-981-19-6880-8_25
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