Mental health concerns, such as depression, pose significant challenges for support systems in effectively identifying affected individuals. On the other hand, people suffering from depression often find it easier to discuss on social media rather than in face-to-face interactions. Additionally, the development of distressing conditions typically arises from a multitude of factors accumulated over time rather than a singular event. To gain a fine-grained understanding of these facets, in this work, we perform a longitudinal analysis of the tweeting behaviour of Indian users who post content related to self-harm. We categorise users based on their posting frequency and examine various aspects including their social network, bio descriptions, tweeting preferences, temporal variations and cognitive indicators. By elucidating these nuances, we aim to contribute insights that could aid in the early detection of mental health issues and prompt timely intervention from support networks.
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Chhikara et al. (2024) studied this question.
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