ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Original Paper

Bursty topic detection method for microblog based on influence from user behaviors

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.04.007
  • Received Date: 28 August 2016
  • Rev Recd Date: 08 December 2016
  • Publish Date: 30 April 2017
  • Social networks are becoming more and more popular where people can post anything anytime. Due to the huge user community, social network data is increasing with each passing day. Therefore how to explore the knowledge in huge data seems to be hard work. As microblog has time-related characteristics and social network behavior attributes, momentum signal enhancement model is put forward to detect bursty microblog topics effectively. Influence factor and hot energy factor are put forward to improve the momentum model. The influence factor uses the data before the current point but within a given period to calculate the
    Social networks are becoming more and more popular where people can post anything anytime. Due to the huge user community, social network data is increasing with each passing day. Therefore how to explore the knowledge in huge data seems to be hard work. As microblog has time-related characteristics and social network behavior attributes, momentum signal enhancement model is put forward to detect bursty microblog topics effectively. Influence factor and hot energy factor are put forward to improve the momentum model. The influence factor uses the data before the current point but within a given period to calculate the
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  • [1]
    GAGLIO S, RE G L, MORANA M. A framework for real-time Twitter data analysis[J]. Computer Communications, 2016, 73: 236-242.
    [2]
    FUNG G P C, YU J X, YU P S, et al. Parameter free bursty events detection in text streams[C]// Proceedings of the 31st International Conference on Very large Data Bases. Trondheim, Norway: VLDB Endowment, 2005: 181-192.
    [3]
    DIAO Q M, JIANG J, ZHU F D, et al. Finding bursty topics from microblogs[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Jeju Island, Korea: Association for Computational Linguistics, 2012: 536-544.
    [4]
    于海峰, 王延章, 卢小丽, 等. 基于知识元的突发事件风险熵预测模型研究[J]. 系统工程学报, 2016, 31(1): 117-126.
    YU Haifeng, WANG Yanzhang, LU Xiaoli, et al. Emergency risk entropy forecasting model based on knowledge element[J]. Journal of Systems Engineering, 2016, 31(1): 117-126.
    [5]
    KLEINBERG J. Bursty and hierarchical structure in streams[J]. Data Mining and Knowledge Discovery, 2003, 7(4): 373-397.
    [6]
    CHEN Y, YANG S, CHENG X Q. Bursty topics extraction for web forums[C]// Proceedings of the 11th International Workshop on Web Information and Data Management. Hong Kong, China: ACM Press, 2009: 55-58.
    [7]
    HE D, PARKER D S. Topic momentums: An alternative model of bursts in streams of topics[C]// Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington D C, USA: ACM Press, 2010: 443-452.
    [8]
    贺敏, 杜攀, 张瑾,等. 基于动量模型的微博突发话题检测方法[J]. 计算机研究与发展, 2015, 52(5): 1022-1028.
    HE Min, DU Pan, ZHANG Jin, et al. Microblog bursty topic detection method based on momentum model[J]. Journal of Computer Research and Development, 2015, 52(5): 1022-1028.
    [9]
    HE D, PARKER D S. Learning the funding momentum of research projects[J]. Knowledge Discovery and Data Mining, 2011, 6635(2):532-543.
    [10]
    DU Y, HE Y, TIAN Y, et al. Microblog bursty topic detection based on user relationship[C]// Proceedings of the 6th IEEE Joint International Information Technology and Artificial Intelligence Conference. Chongqing, China: IEEE Press, 2011, 1: 260-263.
    [11]
    王征, 王林森, 赵磊. 基于信息密度的微博突发话题检测模型研究[J]. 情报理论与实践, 2016, 39(3): 125-129.
    [12]
    申国伟, 杨武, 王巍,等. 面向大规模微博消息流的突发话题检测[J]. 计算机研究与发展, 2015, 52(2): 512-521.
    SHEN Guowei, YANG Wu, WANG Wei, et al. Burst topic detection oriented large-scale microblogs streams[J]. Journal of Computer Research and Development, 2015, 52(2): 512-521.
    [13]
    贺敏, 徐杰, 杜攀, 等. 基于时间序列分析的微博突发话题检测方法[J]. 通信学报, 2016, 37(3): 48-54.
    HE Min, XU Jie, DU Pan, et al. Bursty topic detection method for microblog based on time series analysis[J]. Journal on Communications, 2016, 37(3): 48-54.
    [14]
    郭跇秀, 吕学强, 李卓. 基于突发词聚类的微博突发事件检测方法[J]. 计算机应用, 2014, 34(2): 486-490, 505.
    GUO Yixiu, LYN Xueqiang, LI Zhuo. Bursty topics detection approach on Chinese microblog based on burst words clustering[J]. Journal of Computer Applications, 2014, 34(2): 486-490, 505.
    [15]
    徐志明, 李栋, 刘挺, 等. 微博用户的相似性度量及其应用[J]. 计算机学报, 2014, 37(1): 207-218.
    XU Zhiming, LI Dong, LIU Ting, et al. Measuring similarity between microblog users and its application[J]. Chinese Journal of Computers, 2014, 37(1): 207-218.
    [16]
    毛佳昕, 刘奕群, 张敏, 等. 基于用户行为的微博用户社会影响力分析[J]. 计算机学报, 2014, 37(4): 791-800.
    MAO Jiaxin, LIU Yiqun ZHANG Min, et al. Social influence analysis for micro-blog user based on user behavior[J]. Chinese Journal of Computers, 2014, 37(4): 791-800.
    [17]
    陈克寒, 韩盼盼, 吴健. 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报, 2013, 36(2): 349-359.
    CHEN Kehan, HAN Panpan, WU Jian. User clustering based social network recommendation[J]. Chinese Journal of Computers, 2013, 36(2): 349-359.
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Catalog

    [1]
    GAGLIO S, RE G L, MORANA M. A framework for real-time Twitter data analysis[J]. Computer Communications, 2016, 73: 236-242.
    [2]
    FUNG G P C, YU J X, YU P S, et al. Parameter free bursty events detection in text streams[C]// Proceedings of the 31st International Conference on Very large Data Bases. Trondheim, Norway: VLDB Endowment, 2005: 181-192.
    [3]
    DIAO Q M, JIANG J, ZHU F D, et al. Finding bursty topics from microblogs[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Jeju Island, Korea: Association for Computational Linguistics, 2012: 536-544.
    [4]
    于海峰, 王延章, 卢小丽, 等. 基于知识元的突发事件风险熵预测模型研究[J]. 系统工程学报, 2016, 31(1): 117-126.
    YU Haifeng, WANG Yanzhang, LU Xiaoli, et al. Emergency risk entropy forecasting model based on knowledge element[J]. Journal of Systems Engineering, 2016, 31(1): 117-126.
    [5]
    KLEINBERG J. Bursty and hierarchical structure in streams[J]. Data Mining and Knowledge Discovery, 2003, 7(4): 373-397.
    [6]
    CHEN Y, YANG S, CHENG X Q. Bursty topics extraction for web forums[C]// Proceedings of the 11th International Workshop on Web Information and Data Management. Hong Kong, China: ACM Press, 2009: 55-58.
    [7]
    HE D, PARKER D S. Topic momentums: An alternative model of bursts in streams of topics[C]// Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington D C, USA: ACM Press, 2010: 443-452.
    [8]
    贺敏, 杜攀, 张瑾,等. 基于动量模型的微博突发话题检测方法[J]. 计算机研究与发展, 2015, 52(5): 1022-1028.
    HE Min, DU Pan, ZHANG Jin, et al. Microblog bursty topic detection method based on momentum model[J]. Journal of Computer Research and Development, 2015, 52(5): 1022-1028.
    [9]
    HE D, PARKER D S. Learning the funding momentum of research projects[J]. Knowledge Discovery and Data Mining, 2011, 6635(2):532-543.
    [10]
    DU Y, HE Y, TIAN Y, et al. Microblog bursty topic detection based on user relationship[C]// Proceedings of the 6th IEEE Joint International Information Technology and Artificial Intelligence Conference. Chongqing, China: IEEE Press, 2011, 1: 260-263.
    [11]
    王征, 王林森, 赵磊. 基于信息密度的微博突发话题检测模型研究[J]. 情报理论与实践, 2016, 39(3): 125-129.
    [12]
    申国伟, 杨武, 王巍,等. 面向大规模微博消息流的突发话题检测[J]. 计算机研究与发展, 2015, 52(2): 512-521.
    SHEN Guowei, YANG Wu, WANG Wei, et al. Burst topic detection oriented large-scale microblogs streams[J]. Journal of Computer Research and Development, 2015, 52(2): 512-521.
    [13]
    贺敏, 徐杰, 杜攀, 等. 基于时间序列分析的微博突发话题检测方法[J]. 通信学报, 2016, 37(3): 48-54.
    HE Min, XU Jie, DU Pan, et al. Bursty topic detection method for microblog based on time series analysis[J]. Journal on Communications, 2016, 37(3): 48-54.
    [14]
    郭跇秀, 吕学强, 李卓. 基于突发词聚类的微博突发事件检测方法[J]. 计算机应用, 2014, 34(2): 486-490, 505.
    GUO Yixiu, LYN Xueqiang, LI Zhuo. Bursty topics detection approach on Chinese microblog based on burst words clustering[J]. Journal of Computer Applications, 2014, 34(2): 486-490, 505.
    [15]
    徐志明, 李栋, 刘挺, 等. 微博用户的相似性度量及其应用[J]. 计算机学报, 2014, 37(1): 207-218.
    XU Zhiming, LI Dong, LIU Ting, et al. Measuring similarity between microblog users and its application[J]. Chinese Journal of Computers, 2014, 37(1): 207-218.
    [16]
    毛佳昕, 刘奕群, 张敏, 等. 基于用户行为的微博用户社会影响力分析[J]. 计算机学报, 2014, 37(4): 791-800.
    MAO Jiaxin, LIU Yiqun ZHANG Min, et al. Social influence analysis for micro-blog user based on user behavior[J]. Chinese Journal of Computers, 2014, 37(4): 791-800.
    [17]
    陈克寒, 韩盼盼, 吴健. 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报, 2013, 36(2): 349-359.
    CHEN Kehan, HAN Panpan, WU Jian. User clustering based social network recommendation[J]. Chinese Journal of Computers, 2013, 36(2): 349-359.

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