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In clinical diagnosis, it is crucial and difficult to make a distinction among mental illnesses. As voice is one way of expression, it has been proved that it is a good indicator to separate mental illnesses from healthy people. Though many studies have used acoustic features to identify depression, few of them explored how to identify comorbidities from depressed people. We utilized a secondary audio...
Suicide is becoming a serious problem, and how to prevent suicide has become a very important research topic. The development of Social Network System (SNS) provides an ideal platform to monitor persons' suicidal ideation. Based on Sina microblog (Weibo), this paper proposes a real-time monitoring system detecting users' suicidal ideation. From 59046 posts collected with labels of either suicide or...
With the continuous growth of micro-blog services, Sina Weibo is increasingly found in the daily lives of ordinary Chinese individuals. More than one hundred million tweets are released in Sina Weibo everyday. By analyzing these mass data timely, media companies could learn how to generate buzz for new films, famous stars, or fashion shows more effectively. However, how to predict which topics will...
Early detection of depression is important to improve human well-being. This paper proposes a new method to detect depression through time-frequency analysis of Internet behaviors. We recruited 728 postgraduate students and obtained their scores on a depression questionnaire (Zung Self-rating Depression Scale, SDS) and digital records of Internet behaviors. By time-frequency analysis, we built classification...
With the development of pattern recognition and artificial intelligence, emotion recognition based on facial expression has attracted a great deal of research interest. Facial emotion recognition are mainly based on facial images. The commonly used datasets are created artificially, with obvious facial expression on each facial images. Actually, emotion is a complicated and dynamic process. If a person...
Depression has become a public health concern around the world. Traditional methods for detecting depression rely on self-report techniques, which suffer from inefficient data collection and processing. This paper built both classification and regression models based on linguistic and behavioral features acquired from 10,102 social media users, and compared classification and prediction accuracy respectively...
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 micro blogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from micro...
Personality research on social media is a hot topic recently due to the rapid development of social media as well as the central importance of personality study in psychology, but most studies are conducted on inadequate label samples. Our research aims to explore the usage of unlabeled samples to improve the prediction accuracy. By conducting n user study with 1792 users, we adopt local linear semi-supervised...
Model based movie recommender systems have been thoroughly investigated in the past few years, and they rely on rating data. In this paper, we take into account unrateddata of genre information to improve the performance of movie recommendation. We propose a novel method to measure users' preference on movie genres, and use Pearson Correlation Coefficient(PCC) to compute the user similarity. A matrix...
Personality can be defined as a set of characteristics which makes a person unique. The study of personality is of central importance in psychology. Conventional personality assessment is performed by self-report inventory, which costs much manual efforts and cannot be done in real time. To solve these problems, this research aims to measure the Big-Five personality from the usages of Sina Microblog...
Currently, people around the world are suffering from mental disorders. Given the wide-spread use of the Internet, we propose to predict users' mental health status based on browsing behavior, and further recommend suggestions for adjustment. To identify mental health status, we extract the user's web browsing behavior, and train a Support Vector Machine(SVM) model for prediction. Based on the predicted...
As the development and progress of the society, human beings are suffering from pressure while working, studying, and living. Many researches have been done on cognitive-behavior therapy to solve this problem. In this paper, we introduce our mental health self-help system which applies machine learning to cognitive-behavior therapy. According to the randomized controlled user study, our system is...
To provide a better service, it is important to understand user's behavior on mobile device. In this paper, we propose a framework for logging, MobileSens, to record users' operations on Andriod mobile device, including smart phone and pad. The operations include turning on screen, sending messages, and browsing the web, etc. This system uploads the data to server via GPRS(General Packet Radio Service)...
It is very important to understand web users' psychological characteristics, which can help people adapt this rapid and complicated internet world better. Nowadays, web behavior plays a significant role in people's life and mental health. It is widely accepted in clinical psychology that mental activities and status are expressed in a way of behavior such as preference and choice. Web behavior, as...
It is well known that the online news can trigger different moods in readers. Many researches have been done on reader mood classification of online news, which use the news that have been labeled by readers. However, it is still unknown whether the mood labels on line are consistent with the readers who seldom label any news on the web. In this paper, we describe a user study which is designed to...
Traditionally, questionnaire is used in psychological investigation to collect experiment data. With the prevalence of Internet, web behavior record may provide a new choice for data collection. In this paper, we investigate both web behavior and psychological characteristic, and summarize the worldwide achievement on this direction. We find strong relations between web behavior and the users' personalities...
The traditional approach to detect mental state mainly depends on questionnaires completed by the subjects. The accuracy of answer is affected by many factors including subject's knowledge, the ability of memory, and the authenticity of answer. In order to improve the accuracy, we propose to utilize web behaviors to identify mental state instead of questionnaires. Currently, the web plays a key role...
Nowadays, the development and application of the recommender system has grown greatly to cope with information overloading. Meanwhile, social networh come into being and become more and more popular. In this paper, a recommendation model based on social networks is proposed which can collect the users profile from the feedback and system log, then set up the social networks. According to the input...
When browsing news on the web, various emotions may be evoked in readers and furthermore cause different influence on their minds and life. We expect that emotional analysis and classification of text may provide good performance and significance to users surfing the Internet. Most previous research only focus on bi-emotion classification, that is, Positive and Negative, e.g., identifying whether...
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering method based on semi-supervised learning to get focuses of social topics in a large...
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