Visualization Analysis for Big Data in Computational CyberPsychology

This paper discusses issues related to the big data in Computational CyberPsychology, and proposes to utilize Parallel Coordinates, an famous method in the field of information visualization, to improve analyzing data. The experimental results show that Parallel Coordinates will be very helpful and give visual analyzing of big data in studying of Computational CyberPsychology.</p

To Be Extraverted or Introverted Extravert on Internet Community? A Moderating Role of Using Real-Life Portrait as Facial Picture

As to the relationship between extraverted disposition and preference for social communication on Internet community, there have been two hypotheses: &quot;Social Enhancement&quot;(&quot;Rich Get Richer&quot;) and &quot;Social Compensation&quot; (&quot;Poor Get Richer&quot;). In this paper, we examined the role of using real-life portrait as facial picture in determining whether an extraverted user would be sociable on Internet community. 203 Chinese Social Networking Site (SNS) users were administrated with Big Five Inventory (BFI) to collect their scores on extraversion, and their web use behaviors were downloaded via Application Programming Interfaces (APIs). Results showed that, the factor of using real-life portrait as facial picture played a moderating role in the relationship between scores on extraversion and number of online friends (beta = 0.33, p &lt; 0.05). For people using real-life portrait as facial picture, increased scores on extraversion were associated with increased number of online friends (beta = 0.33, p &lt; 0.001); conversely, for people using none real-life portrait, there were no significant relationship between scores on extraversion and number of online friends (beta = -0.04, p = 0.85).</p

Web Use Behaviors for Identifying Mental Health Status

It is very important to identify mental health problems early and efficiently, but traditional method relies on face-to-face communication which suffers from the limitations in practice. This study aimed to propose an innovative method of detecting mental health problems via web use behaviors. 102 graduates were administrated by SCL-90 questionnaire to get their actual mental health status with 10 dimensions, and their web use behaviors were acquired from Internet access log recorded on the gateway. A computational model for predicting scores on each SCL-90 dimension was built based on web use behaviors. Results indicated that the value of Pearson Correlation Coefficient between predicted scores and actual scores on each dimension ranged from 0.49 to 0.65, and the value of Relative Absolute Error (RAE) ranged from 75% to 89%. It suggests that it is efficient and valid to identify mental health status through web use behaviors, which would improve the performance of mental health care services in the future.</p

Predicting Depression from Internet Behaviors by Timefrequency Features

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 Selfrating Depression Scale, SDS) and digital records of Internet behaviors. By time-frequency analysis, we built classification models for differentiating higher SDS group from lower group and prediction models for identifying mental status of depressed group more precisely. Experimental results show classification and prediction models work well, and time-frequency features are effective in capturing the changes of mental health status. Results of this paper might be useful to improve the performance of public mental health services.</p

Movie Recommendation using Unrated Data

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 unrated data of genre information to improve the performance of movie recommendation. We propose a novel method to measure users&#39; preference on movie genres, and use Pearson Correlation Coefficient (PCC) to compute the user similarity. A matrix factorization framework is introduced for genre preference regularization. Experimental results on MovieLens data set demonstrate that the approach performs well. Our method can also be used to increase the genre diversity of recommendations to some extent.</p

Predicting Mental Health Status in the Context of Web Browsing

Currently, people around the world are suffering from mental disorders. Given the wide-spread use of the Internet, we propose to predict users&#39; mental health status based on browsing behavior, and further recommend suggestions for adjustment. To identify mental health status, we extract the user&#39;s web browsing behavior, and train a Support Vector Machine(SVM) model for prediction. Based on the predicted status, our recommender system generates suggestions for adjusting mental disorders. We have implemented a system named WebMind as the experimental platform integrated with the predicting model and recommendation engine. We have conducted user study to test the effectiveness of the predicting model, and the result demonstrates that the recommender system performs fairly well.</p

Heterogeneous Domain Adaptation Using Linear Kernel

When a task of a certain domain doesn&#39;t have enough labels and good features, traditional supervised learning methods usually behave poorly. Transfer learning addresses this problem, which transfers data and knowledge from a related domain to improve the learning performance of the target task. Sometimes, the related task and the target task have the same labels, but have different data distributions and heterogeneous features. In this paper, we propose a general heterogeneous transfer learning framework which combines linear kernel and graph regulation. Linear kernel is used to project the original data of both domains to a Reproducing Kernel Hilbert Space, in which both tasks have the same feature dimensions and close distance of data distributions. Graph regulation is designed to preserve geometric structure of data. We present the algorithms in both unsupervised and supervised way. Experiments on synthetic dataset and real dataset about user web-behavior and personality are performed, and the effectiveness of our method is demonstrated.</p

Predicting the Trends of Social Events on Chinese Social Media

Growing interest in social events on social media came along with the rapid development of the Internet. Social events that occur in the "real" world can spread on social media (e.g., Sina Weibo) rapidly, which may trigger severe consequences and thus require the government's timely attention and responses. This article proposes to predict the trends of social events on Sina Weibo, which is currently the most popular social media in China. Based on the theories of social psychology and communication sciences, we extract an unprecedented amount of comprehensive and effective features that relate to the trends of social events on Chinese social media, and we construct the trends of prediction models by using three classical regression algorithms. We found that lasso regression performed better with the precision 0.78 and the recall 0.88. The results of our experiments demonstrated the effectiveness of our proposed approach

Realtime Online Hot Topics Prediction in Sina Weibo for News Earlier Report

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 be the most popular search terms in Sina Weibo in realtime remains unknown. In this paper, we present a realtime hot topic prediction method in an online platform. Experiments are carried out on the platform to evaluate the proposed scheme. The results show that our model gets an average precision 44.32% and the median value is 45.83%. The proposed hot topic prediction method can predict the hot topics about 9.5 hours in average in advance.</p

Your Search Behavior and Your Personality

Search is very important to acquire useful information from the Web. To provide better search service, we need to look into how people conduct search. In this paper, we focus on web search behavior, and try to identify how it relates to the personality traits, then investigate the potential personality predicting model based on search behaviors. Several features are extracted from web search behavior, corresponding personality-trait scores are obtained, too. Correlation analysis method is used to deal with the data, the results show that part of searching behaviors are correlated with personality traits in some degree.</p