ZHANG Xiao-wei1, YU Yun-xia2, WANG Wei1. On Prediction and Analysis of Social Network Group User Activity[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(12): 115-121. doi: 10.13718/j.cnki.xsxb.2018.12.019
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ZHANG Xiao-wei1, YU Yun-xia2, WANG Wei1. On Prediction and Analysis of Social Network Group User Activity[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(12): 115-121. doi: 10.13718/j.cnki.xsxb.2018.12.019
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On Prediction and Analysis of Social Network Group User Activity
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1. School of Network Engineer, Zhoukou Normal University, Zhoukou Henan 466001, China;
2. School of Computer Engineering, Jingchu University of Technology, Jingmen Hubei 448000, China
More Information
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Received Date:
21/12/2017
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Abstract
For different groups in a social network, their activities are frequently influenced by a variety of factors such as user's attributes (i.e., gender, age), group classes, social relationships between group members and so on. In order to model and analyze the activities of different groups in this paper, several features which may influence the activity of a group have first been extracted from the historical data generated from a social network, such as census information, social relationships, group class, user stickiness (the number of the shared photos and information) and so on. Then, based on the extracted features, the activity of a group is predicted using logistic regression model, support vector machine and BP neural network. The results show that BP neural network has high performance on classifying group users' activity, and social relationships have a major impact on the activity of a group.
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