Detecting community pacemakers of burst topic in Twitter
Twitter has become one of largest social networks for users to broad-cast 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 influen...
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sg-smu-ink.sis_research-44482020-03-30T05:26:11Z Detecting community pacemakers of burst topic in Twitter DONG, Guozhong YANG, Wu ZHU, Feida WANG, Wei Twitter has become one of largest social networks for users to broad-cast 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. 2016-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3447 info:doi/10.1007/978-3-319-45814-4_20 https://ink.library.smu.edu.sg/context/sis_research/article/4448/viewcontent/Detecting_Community_Pacemakers_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Twitter Burst topic User graph model Community pacemakers Databases and Information Systems Social Media |
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Twitter Burst topic User graph model Community pacemakers Databases and Information Systems Social Media DONG, Guozhong YANG, Wu ZHU, Feida WANG, Wei Detecting community pacemakers of burst topic in Twitter |
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Twitter has become one of largest social networks for users to broad-cast 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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DONG, Guozhong YANG, Wu ZHU, Feida WANG, Wei |
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DONG, Guozhong YANG, Wu ZHU, Feida WANG, Wei |
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DONG, Guozhong |
title |
Detecting community pacemakers of burst topic in Twitter |
title_short |
Detecting community pacemakers of burst topic in Twitter |
title_full |
Detecting community pacemakers of burst topic in Twitter |
title_fullStr |
Detecting community pacemakers of burst topic in Twitter |
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Detecting community pacemakers of burst topic in Twitter |
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detecting community pacemakers of burst topic in twitter |
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Institutional Knowledge at Singapore Management University |
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2016 |
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https://ink.library.smu.edu.sg/sis_research/3447 https://ink.library.smu.edu.sg/context/sis_research/article/4448/viewcontent/Detecting_Community_Pacemakers_afv.pdf |
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