Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

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%.Comment: 6 page

Automatic Conditional Generation of Personalized Social Media Short Texts

Automatic text generation has received much attention owing to rapid development of deep neural networks. In general, text generation systems based on statistical language model will not consider anthropomorphic characteristics, which results in machine-like generated texts. To fill the gap, we propose a conditional language generation model with Big Five Personality (BFP) feature vectors as input context, which writes human-like short texts. The short text generator consists of a layer of long short memory network (LSTM), where a BFP feature vector is concatenated as one part of input for each cell. To enable supervised training generation model, a text classification model based convolution neural network (CNN) has been used to prepare BFP-tagged Chinese micro-blog corpora. Validated by a BFP linguistic computational model, our generated Chinese short texts exhibit discriminative personality styles, which are also syntactically correct and semantically smooth with appropriate emoticons. With combination of natural language generation with psychological linguistics, our proposed BFP-dependent text generation model can be widely used for individualization in machine translation, image caption, dialogue generation and so on.Comment: published in PRICAI 201

Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism, co-task aware attention, enables automatic selection of optimal information across the BERT layers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model\u27s robustness and reliability for distinguishing the depression symptoms

The production of distinction: a study of classed subjectivities in an international school in provincial India

This thesis examines the production of classed subjectivities in an international school in provincial India. The relationship of schooling with social class is a relatively unexplored area in the Indian educational research context. Further, in addressing the everyday practices of an international school in provincial India, this study addresses a major research lacuna. The thesis is based on an ethnographic study conducted within an International Baccalaureate school from August 2015 to May 2016. The chief participants were first year students of the International Baccalaureate Diploma Programme in a school in Coimbatore district, a provincial region in Tamil Nadu. Its clientele comprised of professional, industrial and business families from the dominant caste groups of the region, including Gounders, Naidus, Marwaris and Brahmans. During the fieldwork I conducted extensive observations of classroom and wider school activities, as well as interviews with students, parents and school staff. I also used questionnaires to probe students’ perspectives on their education. The analysis of my ethnographic data drew predominantly on post-structural theoretical perspectives that understand class as discursively produced and intersecting with caste and gender. The analysis highlights how sophisticated disciplinary technologies were deployed in the school to produce a ‘self-regulated’ subject. It describes the different practices in the school through which students gained distinction. These included speaking English in de-indigenised ways, demonstrating mastery over technology and constructing a self-narrative which valorised the self through claims to various capitals. Through such practices, a ‘good student’ subject was produced, constructed as capable of successfully navigating the globalising world. Here, while identification with western nations was central to students’ claims to distinction, the nation was conspicuously missing in their symbolic world. On the other hand, students’ family contexts remained significant to their educational and occupational imaginaries. These were markedly gendered and conformed to the dominant caste regimes in the region. Students’ aspirational imaginaries were also shaped by the dominant culture of privatised higher education in the region. In addition to theoretical and methodological contributions, my study illuminates the educational practices of non-traditional middle classes in provincial India and underlines the need to situate the academic narrative about the Indian middle classes in specific contexts. It powerfully highlights the misrecognitions at work in the ways schooling contributes to the production of privileged identities, by unpacking how social hierarchies get re-written in the language of individual abilities. In presenting an intersectional analysis, my thesis also contributes to a complexified understanding of how schooling is related to larger forces of the state, market and traditional gender and caste regimes. Finally, it highlights the shifting truth regimes in this context where an understanding of education as a market commodity is fast gaining currency