Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors

Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

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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

Psychological Health Status Evaluation of the Public in Different Areas Under the Outbreak of Novel Coronavirus Pneumonia

During the outbreak of novel coronavirus pneumonia, the number of confirmed cases and deaths in Hubei province of China increased sharply, and the situation in Hubei was more severe than that in non-Hubei, so we do a research on psychological health status evaluation of the public in Hubei and non-Hubei areas. In this paper, we adopt textual analysis and contextual analysis using Simplified Chinese Microblog Word Count (SCMBWC), Five-Factors Model (FFM), Semantic Role Labeling (SRL) to interpret and analyze the public perception and psychological personality based on media news. Through the analysis, it was found that there were great differences in public perception to novel coronavirus pneumonia. In Hubei areas, the public perception was mainly reflected in the overall prevention and the treatment of patients, while in non-Hubei areas, the perception was mainly in the orderly promotion of enterprises to return to work. Through contextual analysis, the novel coronavirus pneumonia had a great psychological impact on the public in different regions. The media covered a large number of social process words and cognitive process words, public showed a personality that was inclined to be “open” and “neurotic” in different areas. Furthermore, we find out some reasons like all kinds of rumors, wildlife trade, all kinds of illegal and criminal acts disturbing social order cause this psychology personality through emotional entity mining based on semantic role labeling. This is conducive to the government’s better policies and management in line with local conditions

Developing a LIWC Dictionary: The lyrical poetry of 2PAC, Frank Waln, Litefoot, and Nataanii Means

The researchers developed a dictionary, DKL-MN2016, to use the LIWC (http://liwc.wpengine.com/) software to analyze the lyrics of 4 activist, rapper, hiphop artists who are men of color, to answer two research questions: Do Native American male rappers address socioeconomic issues in their lyrics? and How does the lyrical content of Native American male rappers compare Tupac Shakur’s lyrical content written before 1996