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| Identifying Core Users in Online Knowledge Community by Integrating Multiple User Attributes: Based on the Emotion-Weighted LeaderRank Algorithm |
| Yang Ruixian1,2, Yu Zhengjie3, Zhong Qian4, Liu Lili1, Wei Huanan3 |
1.School of Information Management, Zhengzhou University, Zhengzhou 450001
2.Research Institute of Data Science, Zhengzhou City, Zhengzhou 450001
3.Central Big Data Innovation Center, China Academy of Information and Communications Technology, Zhengzhou 450001
4.School of Information Management, Sun Yat-sen University, Guangzhou 510006 |
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Abstract Based on the analysis of user attributes in network knowledge communities, a core user identification method that integrates multiple user attributes is proposed to improve the efficiency and effect of core user identification and provide theoretical and methodological reference for improving community operation and management levels. First, based on the user’s basic attribute data, the user’s activity and professionalism are quantified. Second, a hypernetwork model of the online knowledge community is constructed. An algorithm for the importance of user social relations based on the overlap of neighboring friends, a method for calculating cumulative interaction emotions in user interaction activities, and a ranking algorithm for user comprehensive emotional orientation are proposed. Finally, the entropy weight method is used to integrate the above indicators as the user’s core score, and core users are identified by sorting the scores. The results of empirical research indicate that, compared with the degree centrality ranking in the user social relationship network and the LeaderRank ranking in the user interaction relationship network, the method for identifying core users in the online knowledge community by integrating multiple attributes proposed in this study has better recognition effects.
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Received: 25 September 2023
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