Zengda Guan

• Shandong Jianzhu University

It is important to acquire web users’ psychological characteristics. Recent studies have built computational models for predicting psychological characteristics by supervised learning. However, the generalization of built models might be limited due to the differences in distribution between the training and test dataset. To address this problem, w...

Some research has been done to predict users’ personality based on their web behaviors. They usually use supervised learning methods to model on training dataset and predict on test dataset. However, when training dataset has different distributions from test dataset, which doesn’t meet independently identical distribution condition, traditional su...

Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big...

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. Se...

Computational CyberPsychology deals with web users' behaviors, and identifying their psychology characteristics using machine learning. Transfer learning intends to solve learning problems in target domain with different but related data distributions or features compared to the source domain, and usually the source domain has plenty of labeled dat...

To build a predicting model for mental health status based on Web Usage Behavior, we collect data from 571 first-year graduate students using our own Internet Usage Behavior Check-List (IUBCL) and Psychological Health Inventory (PHI). We build six logistic regression models, in which Web usage behavior features are as independent variables while me...