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Register a new userLatent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention
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Added by
Lei Cao
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Despite detection of suicidal ideation on social media has made great progress in recent years, peoples implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance. Enlightened by the hidden tree holes phenomenon on microblog, where people at suicide risk tend to disclose their inner real feelings and thoughts to the microblog space whose authors have committed suicide, we explore the use of tree holes to enhance microblog-based suicide risk detection from the following two perspectives. (1) We build suicide-oriented word embeddings based on tree hole contents to strength the sensibility of suicide-related lexicons and context based on tree hole contents. (2) A two-layered attention mechanism is deployed to grasp intermittently changing points from individuals open blog streams, revealing ones inner emotional world more or less. Our experimental results show that with suicide-oriented word embeddings and attention, microblog-based suicide risk detection can achieve over 91% accuracy. A large-scale well-labelled suicide data set is also reported in the paper.
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Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk. Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) that can be indicative of someones state of mind or interest regarding mental health disorders such as r/SuicideWatch, r/Anxiety, r/depression. A challenge for previous work on suicide risk assessment has been the small amount of labeled data. We propose an empirical investigation into several classes of weakly-supervised approaches, and show that using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) helps improve model performance for suicide risk assessment.
Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent topic models, on the other hand, take a more global view, looking at the word distributions across the corpus to assign a topic to each word occurrence. These two paradigms are complementary in how they represent the meaning of word occurrences. While some previous works have already looked at using word embeddings for improving the quality of latent topics, and conversely, at using latent topics for improving word embeddings, such two-step methods cannot capture the mutual interaction between the two paradigms. In this paper, we propose STE, a framework which can learn word embeddings and latent topics in a unified manner. STE naturally obtains topic-specific word embeddings, and thus addresses the issue of polysemy. At the same time, it also learns the term distributions of the topics, and the topic distributions of the documents. Our experimental results demonstrate that the STE model can indeed generate useful topic-specific word embeddings and coherent latent topics in an effective and efficient way.
Suicide is the 10th leading cause of death in the US and the 2nd leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (~30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and other important SRFs contributing to suicide risk were missing from both platforms.
This work presents an unsupervised approach for improving WordNet that builds upon recent advances in document and sense representation via distributional semantics. We apply our methods to construct Wordnets in French and Russian, languages which both lack good manual constructions.1 These are evaluated on two new 600-word test sets for word-to-synset matching and found to improve greatly upon synset recall, outperforming the best automated Wordnets in F-score. Our methods require very few linguistic resources, thus being applicable for Wordnet construction in low-resources languages, and may further be applied to sense clustering and other Wordnet improvements.
For most intelligent assistant systems, it is essential to have a mechanism that detects out-of-domain (OOD) utterances automatically to handle noisy input properly. One typical approach would be introducing a separate class that contains OOD utterance examples combined with in-domain text samples into the classifier. However, since OOD utterances are usually unseen to the training datasets, the detection performance largely depends on the quality of the attached OOD text data with restricted sizes of samples due to computing limits. In this paper, we study how augmented OOD data based on sampling impact OOD utterance detection with a small sample size. We hypothesize that OOD utterance samples chosen randomly can increase the coverage of unknown OOD utterance space and enhance detection accuracy if they are more dispersed. Experiments show that given the same dataset with the same OOD sample size, the OOD utterance detection performance improves when OOD samples are more spread-out.
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