基于时间序列分析的微博突发话题检测方法

贺敏; 徐杰; 杜攀; 程学旗; 王丽宏

doi:10.11959/j.issn.1000-436x.2016052

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基于时间序列分析的微博突发话题检测方法

学术论文 | 更新时间：2024-06-05

- 基于时间序列分析的微博突发话题检测方法
- Bursty topic detection method for microblog based on time series analysis
- 通信学报 2016年37卷第3期页码：48-54
- 作者机构：
  
  1. 中国科学院计算技术研究所，北京100080
  2. 国家计算机网络应急技术处理协调中心，北京100029
- 作者简介：
  
  [ "贺敏（1982-），女，山西忻州人，中国科学院计算技术研究所博士生，主要研究方向为网络信息安全、舆情分析、自然语言处理等。" ]
  [ "徐杰（1982-），男，山西五寨人，博士，国家计算机网络应急技术处理协调中心工程师，主要研究方向为网络信息安全和多媒体技术。" ]
  [ "杜攀（1981-），男，河南南阳人，中国科学院计算技术研究所助理研究员，主要研究方向为文本挖掘、信息检索、机器学习等。" ]
  [ "程学旗（1971-），男，安徽安庆人，中国科学院计算技术研究所研究员、博士生导师，主要研究方向为信息检索、文本挖掘、社会计算等。" ]
  [ "王丽宏（1967-），女，辽宁沈阳人，国家计算机网络应急技术处理协调中心副总工程师、研究员，主要研究方向为网络信息安全、舆情分析等。" ]
- 基金信息：
  
  国家高技术研究发展计划（“863”计划）基金资助项目(2014AA015203);国家科技支撑计划基金资助项目(2012BAH46B01)
- DOI：10.11959/j.issn.1000-436x.2016052
  中图分类号： TP391
- 网络出版日期：2016-03，
  
  纸质出版日期：2016-03-25
- 稿件说明：
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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.

Min HE, Jie2 XU, Pan1 DU, et al. Bursty topic detection method for microblog based on time series analysis[J]. Journal on communications, 2016, 37(3): 48-54.
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. DOI： 10.11959/j.issn.1000-436x.2016052.

Min HE, Jie2 XU, Pan1 DU, et al. Bursty topic detection method for microblog based on time series analysis[J]. Journal on communications, 2016, 37(3): 48-54. DOI： 10.11959/j.issn.1000-436x.2016052.

摘要

针对微博信息噪音大、新颖度难以判断的问题，在动量模型的基础上进行优化，提出了基于时序分析的微博突发话题检测方法。通过动量模型提取候选突发特征后，对特征的动量时间序列分别借鉴信号频域分析理论和股票趋势分析理论进行建模，分析特征的频域特性来识别频繁伪突发特征，分析特征的新颖程度来识别间歇性伪突发特征，合并过滤后的有效突发特征形成突发话题。微博数据实验表明，该方法有效提高了突发话题检测的准确率和F值。

Abstract

Detecting bursty topics from microblogs was an important task to understand the current events attracting a large number of internet users.However

the existing hods suitable for news articles cannot be adopted directly for microblogs.Because microblogs have unique characteristics compared wi formal texts

including diversity

dynamic and noise.A detection method for microblog bursty topic was proposed based on time series analysis

which was an op-timization method of momentum model.The candidate bursty features were extracted by momentum model.The time se-ries of feature's momentum were modled by frequency domain analysis theory and stock trend analysis theory.The fre-quently pseudo-bursty features were filtered according to analysis results of frequency-domain characteristics.The inter-mittently pseudo-bursty features were filtered according to the novelty analysis result through stock trend theory.The bursty topics were finally emerged with combination of effective bursty features.The experiments are conducted on a real Sina microblog data set.It show that the proposed method improves the precis and F-measure remarkably compared with the momentum modle.

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references

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