The rising rate of mental health issues in the digital age underscores the critical need for proactive interventions to assess an individual’s well-being. This problem is further exacerbated by the social stigma surrounding the subject, which suppresses the willingness of victims to seek help. Social media can serve as an outlet for such individuals to express their negative emotions or thoughts of self-harm. The social media account of an individual can offer a plethora of valuable information that can be used to predict their mental health. By unifying principles of robust classifier training and selective classification, we propose a novel framework, SAFENet, to predict the suicide risk of users by using their historical social media posts. When the confidence of prediction is low or the individual is classified as a high-risk user, SAFENet delegates the analysis of the posts to a human evaluator for further intervention. Our experiments show that SAFENet outperforms existing state-of-the-art frameworks. We further qualitatively analyze predictions from SAFENet and demonstrate that it performs robustly on difficult samples that may cause contemporary methods to make errors. Our system addresses the urgent need for efficient and effective mental health intervention in the digital era.

The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.