June 11, 2024, 4:43 a.m. | Hongzhi Qi, Qing Zhao, Jianqiang Li, Changwei Song, Wei Zhai, Dan Luo, Shuo Liu, Yi Jing Yu, Fan Wang, Huijing Zou, Bing Xiang Yang, Guanghui Fu

cs.CL updates on arXiv.org arxiv.org

arXiv:2309.03564v3 Announce Type: replace
Abstract: On social media, users often express their personal feelings, which may exhibit cognitive distortions or even suicidal tendencies on certain specific topics. Early recognition of these signs is critical for effective psychological intervention. In this paper, we introduce two novel datasets from Chinese social media: SOS-HL-1K for suicidal risk classification and SocialCD-3K for cognitive distortions detection. The SOS-HL-1K dataset contained 1,249 posts and SocialCD-3K dataset was a multi-label classification dataset that containing 3,407 posts. We …

abstract arxiv benchmarks chinese cognitive cs.cl cs.lg datasets express feelings health language language model large language large language model media mental health recognition replace risks social social media supervised learning topics type

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