Abstract
In today’s world, depression among people is becoming very common, and according to World Health Organization (WHO) by 2020, depression will become second leading cause to death and disability. Depression can be measured in humans by measuring their daily activities and physical conditions, and it is not that easy and has been a challenge in recent days. Peoples are not likely to share their true inner feelings/emotions with anyone easily, so it is important to detect human depression by any machine rather than human being and provide support system immediately based on depression level. The social networks have been developed as a great spot for its utilizers to communicate with their interested friends, and this is a location where they share their beliefs snapshots, and videos reflecting their frame of mind, feelings, and sentiments toward other people as well as the community. This provides us with an idea to examine these information media’s data for user’s feelings and views which would help us to investigate their moods, attitudes, and stubbornness when they are communicating via these online tools. To investigate depression, we put forward machine learning technique as an effective and scalable method.
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Verma, P., Sharma, K., Walia, G.S. (2021). Depression Detection Among Social Media Users Using Machine Learning. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_72
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DOI: https://doi.org/10.1007/978-981-15-5113-0_72
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