Abstract
Twitter has become one of largest social networks for users to broadcast burst topics. Influential users usually have a large number of followers and play an important role in the diffusion of burst topic. There have been many studies on how to detect influential users. However, traditional influential users detection approaches have largely ignored influential users in user community. In this paper, we investigate the problem of detecting community pacemakers. Community pacemakers are defined as the influential users that promote early diffusion in the user community of burst topic. To solve this problem, we present DCPBT, a framework that can detect community pacemakers in burst topics. In DCPBT, a burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, a user community detection algorithm based on random walk is applied to discover user community. For large-scale user community, we propose a ranking method to detect community pacemakers in each large-scale user community. To test our framework, we conduct the framework over Twitter burst topic detection system. Experimental results show that our method is more effective to detect the users that influence other users and promote early diffusion in the early stages of burst topic.
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Acknowledgment
This work is supported by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation, China Scholarship Council, the Fundamental Research Funds for the Central Universities (no. HEUCF100605), the National High Technology Research and Development Program of China (no. 2012AA012802) and the National Natural Science Foundation of China (no. 61170242, no. 61572459); the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiative and Pinnacle Lab for Analytics at Singapore Management University.
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Dong, G., Yang, W., Zhu, F., Wang, W. (2016). Detecting Community Pacemakers of Burst Topic in Twitter. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_20
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DOI: https://doi.org/10.1007/978-3-319-45814-4_20
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