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Depression is the most prevalent mental disorder globally, but traditional sources of depression help seeking such as psychiatry are laced with social stigma, resulting in a low worldwide depression professional help seeking rate. In recent years, many with depression have turned to the internet and specifically to social media as an alternative medium to seek help for depression, but the effects of online help have been discovered to be ambiguous. The present study aimed to investigate how depression help seeking related posts are responded to on Chinese social media in terms of two dimensions: themes and sentiments. Employing machine learning statistical techniques including topic modeling and sentiment analysis, this paper assessed the themes and sentiments within 8027 responses to 648 depression help seeking related posts created between October 1, 2018 and June 30, 2024. Topic modeling results reported that four prevalent themes underlie replies to depression help seeking related posts: Sad emotional support, Empathizing, Advising professional help, and Sharing. These prevalent themes all reflected supportive attitudes towards depression help seeking related posts through diverse ways. Sentiment analysis, on the other hand, revealed that still, 18.34% of responses held negative sentiments toward depression help seeking related posts. However, the prevalent sentiment in responses was also discovered to be positive, in line with the results of topic modeling. 78.04% of replies were found to hold positive sentiment, 3.63% of replies were found to hold neutral sentiment, and the mean sentiment score of responses was also positive. The findings of the present study provide insight into how depression help seeking is reacted to in Chinas social media landscape, indicating possible factors that may hinder the effects of seeking help for depression on social media and suggesting the potential need for systems to foster a more supportive online environment towards depression help seeking on Chinese social media.
Ziyao Chen (Sat,) studied this question.