Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68.3%, a Precision of 78.9%, and a Recall of 60.3%.

Superpixel segmentation is a kind of image preprocessing technology and a popular research direction in image processing. The purpose of superpixel segmentation is to reduce the complexity of image processing. The most widely applied Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm has high operating efficiency. However, under-segmentation is prone to occur when the number of given superpixel regions is too small. In order to improve the segmentation accuracy, the superpixel segmentation algorithm based on local network modularity increment (LocalNet) from the perspective of network community detection is proposed here. The adjacency network is constructed according to the colour similarity of image pixels, the local community centre is found by the degree of network nodes, and the local network structure of the community is constructed. The modularity increment is employed as the boundary constraint to improve the segmentation accuracy of superpixel segmentation. Through the experimental comparison with the SLIC algorithm, its improved algorithm, and the algorithm proposed in recent years, the results show that our LocalNet algorithm significantly improves in segmentation accuracy Furthermore, the segmentation effect has obvious advantages under the premise that the segmentation speed is not much different from that of the other five algorithms.

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