Abstract
Online social network has turned out to have widespread existence on the Internet gradually. Social network services allow its users to stay connected globally, help the content makers to grow their business, etc. However, it also causes some possible risks to susceptible users of these media, for instance, the rapid increase of suicidal ideation in the online social networks. It has been found that many at-risk users use social media to express their feelings before taking more drastic step. Hence, timely identification and detection are considered to be the most efficient approach for suicidal ideation prevention and subsequently suicidal attempts. In this paper, a summarized view of different approaches such as machine learning or deep learning approaches, used to detect suicidal ideation through online social network data for automated detection, is presented. Also, the type of features used and the feature extraction methods for suicidal ideation detection are discussed in this paper. A comparative study of the different approaches to detect suicidal ideation is provided along with the shortcomings of the current works, and future research direction in this area is discussed in this paper.
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Chandra, S., Bhattacharya, S., Banerjee(Ghosh), A., Kundu, S. (2021). Suicide Ideation Detection in Online Social Networks: A Comparative Review. In: Mandal, J.K., Mukhopadhyay, S., Unal, A., Sen, S.K. (eds) Proceedings of International Conference on Innovations in Software Architecture and Computational Systems. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-4301-9_12
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DOI: https://doi.org/10.1007/978-981-16-4301-9_12
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