Suicide risk level prediction and suicide trigger detection: a benchmark dataset

Abstract

The increasing number of suicide events in recent years has set off an alarm in society. To prevent suicides, it is important to develop platforms with associated intelligent algorithms, such as those for early suicide ideation detection (SID). In general, entering people’s lives to obtain indicative information for effective SID is prohibitive due to the possible risk of privacy invasion. Social media posts provide valuable information about users’ activities, indicating important hints toward SID in a non-intrusive manner. Although multiple datasets have been collected from social media platforms for efficient SID, they either neglect many suicidal posts by searching for pre-defined keywords, or contain limited information about suicide ideation. In this paper, a newly collected dataset, which expands the coverage of suicidal posts with more fine-grained annotations of suicide risk levels and suicide triggers compared with existing datasets, is presented. Benchmarking results by a popular deep-learning model are analysed to validate the reliability and potential of the collected dataset. Furthermore, multiple application scenarios of the dataset are discussed. The proposed dataset is expected to enhance the research on suicide ideation and behaviours, and have a strong impact on SID and more importantly, suicide prevention to save invaluable lives.