With a surge in identifying suicidal risk and its severity in social media posts, we argue that a more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare. The success of computational intelligence techniques for inferring mental illness from social media resources, points to natural language processing as a lens for determining Interpersonal Risk Factors (IRF) in human writings. Motivated with limited availability of datasets for social NLP research community, we construct and release a new annotated dataset with human-labelled explanations and classification of IRF affecting mental disturbance on social media: (i) Thwarted Belongingness (TBe), and (ii) Perceived Burdensomeness (PBu). We establish baseline models on our dataset facilitating future research directions to develop real-time personalized AI models by detecting patterns of TBe and PBu in emotional spectrum of user's historical social media profile.

翻译：随着社交媒体帖子中自杀风险及其严重性识别的激增，我们认为需要开展更具影响力和可解释性的研究，以优化对临床心理学实践和个性化心理保健的作用。计算智能技术从社交媒体中推断精神疾病的成功表明，自然语言处理可作为识别人类写作中人际关系风险因素（IRF）的透镜。鉴于社交自然语言处理研究社区可用的数据集有限，我们构建并发布了一个新的标注数据集，其中包含人工标注的解释和影响社交媒体心理困扰的IRF分类：（i）挫败的归属感（TBe）和（ii）感知的累赘感（PBu）。我们基于数据集建立了基线模型，通过检测用户历史社交媒体档案情感谱中的TBe和PBu模式，为未来开发实时个性化人工智能模型的研究方向提供支持。