Purpose： We conducted an empirical study to find out the role of demographic variables in affecting information sharing behaviors of college student users of WeChat.Design/methodology/approach： A questionnaire surve...Purpose： We conducted an empirical study to find out the role of demographic variables in affecting information sharing behaviors of college student users of WeChat.Design/methodology/approach： A questionnaire survey was carried out to investigate the relationship between demographic variables （gender, grade level, dating status, and singleparent family background） and information sharing behaviors of WeChat users. The participants were college students and a total of 255 valid questionnaires were collected. Data was analyzed using descriptive statistics and multiple regression analysis.Findings： Grade level and single-parent family background were found to be significantly correlated with information sharing behaviors whereas no effect of gender was found on information sharing behaviors. Dating status had no significant impact on user browsing behavior, but was related to users＇ publishing posts and posting replies.Implications： The study will help understand individual differences in information sharing among WeChat users.Limitations： First, the relatively small sample size is a limitation in exploring the effects of demographic variables on user information sharing behaviors. Second, this study only used questionnaire surveys to collect data and more research methods such as interviews should be adopted to improve the accuracy of the study results.Originality/value： This paper is one of the first studies to explore the relationship between demographic variables and user information sharing behaviors on WeChat.展开更多
Traditional Belief-Rule-Based(BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems particip...Traditional Belief-Rule-Based(BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems participating in ensemble learning increases, a large amount of redundant sub-BRB systems are generated because of the diminishing difference between subsystems. This drastically decreases the prediction speed and increases the storage requirements for BRB systems. In order to solve these problems, this paper proposes BRBCS-PAES: a selective ensemble learning approach for BRB Classification Systems(BRBCS) based on ParetoArchived Evolutionary Strategy(PAES) multi-objective optimization. This system employs the improved Bagging algorithm to train the base classifier. For the purpose of increasing the degree of difference in the integration of the base classifier, the training set is constructed by the repeated sampling of data. In the base classifier selection stage, the trained base classifier is binary coded, and the number of base classifiers participating in integration and generalization error of the base classifier is used as the objective function for multi-objective optimization. Finally,the elite retention strategy and the adaptive mesh algorithm are adopted to produce the PAES optimal solution set.Three experimental studies on classification problems are performed to verify the effectiveness of the proposed method. The comparison results demonstrate that the proposed method can effectively reduce the number of base classifiers participating in the integration and improve the accuracy of BRBCS.展开更多
基金jointly supported by the National Social Science Foundation of China(Grant No.:14BTQ044)Wuhan University Academic Development Plan for Scholars born after the 1970s for the project of"research on Internet user behavior"Wuhan University Postgraduate English Course on Internet User Behavior and Luo Jia Youth Scholar of Wuhan University
文摘Purpose： We conducted an empirical study to find out the role of demographic variables in affecting information sharing behaviors of college student users of WeChat.Design/methodology/approach： A questionnaire survey was carried out to investigate the relationship between demographic variables （gender, grade level, dating status, and singleparent family background） and information sharing behaviors of WeChat users. The participants were college students and a total of 255 valid questionnaires were collected. Data was analyzed using descriptive statistics and multiple regression analysis.Findings： Grade level and single-parent family background were found to be significantly correlated with information sharing behaviors whereas no effect of gender was found on information sharing behaviors. Dating status had no significant impact on user browsing behavior, but was related to users＇ publishing posts and posting replies.Implications： The study will help understand individual differences in information sharing among WeChat users.Limitations： First, the relatively small sample size is a limitation in exploring the effects of demographic variables on user information sharing behaviors. Second, this study only used questionnaire surveys to collect data and more research methods such as interviews should be adopted to improve the accuracy of the study results.Originality/value： This paper is one of the first studies to explore the relationship between demographic variables and user information sharing behaviors on WeChat.
基金supported by the National Natural Science Foundation of China (Nos. 71501047 and 61773123)the Natural Science Foundation of Fujian Province (No. 2019J01647)
文摘Traditional Belief-Rule-Based(BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems participating in ensemble learning increases, a large amount of redundant sub-BRB systems are generated because of the diminishing difference between subsystems. This drastically decreases the prediction speed and increases the storage requirements for BRB systems. In order to solve these problems, this paper proposes BRBCS-PAES: a selective ensemble learning approach for BRB Classification Systems(BRBCS) based on ParetoArchived Evolutionary Strategy(PAES) multi-objective optimization. This system employs the improved Bagging algorithm to train the base classifier. For the purpose of increasing the degree of difference in the integration of the base classifier, the training set is constructed by the repeated sampling of data. In the base classifier selection stage, the trained base classifier is binary coded, and the number of base classifiers participating in integration and generalization error of the base classifier is used as the objective function for multi-objective optimization. Finally,the elite retention strategy and the adaptive mesh algorithm are adopted to produce the PAES optimal solution set.Three experimental studies on classification problems are performed to verify the effectiveness of the proposed method. The comparison results demonstrate that the proposed method can effectively reduce the number of base classifiers participating in the integration and improve the accuracy of BRBCS.
