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Topic representation model based on microblogging behavior analysis

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Abstract

With the development of microblogging, it has become an important way for people to obtain information, express opinions, and make suggestions. Identifying new topics quickly and accurately from the massive microblogging data plays a crucial role for recommending information and controlling public opinion. The topic representation model provides a basis for topic detection. In this paper, we propose a topic representation model based on user behavior analysis, i.e., microblogging behavior analysis-latent Dirichlet allocation (MBA-LDA) model, for microblogging datasets. Topic-word distribution is acquired by the LDA model which considers information on user behaviors (such as posting, forwarding and commenting) and word distribution among documents within one topic and among different topics. The model also re-assesses the importance of words in topic representation. The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is the common word of all topics, and it can’t achieve the purpose of distinguishing topics, so it is less suitable to represent any topic. By experiments with Sina Microblogging’s actual data set, the topic model based on the MBA-LDA algorithm makes the representative words more important and increases the differentiation of topic words, which effectively improves the accuracy of subsequent topic detection and evolutionary analysis.

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Funding

Funded by NSFC (No. 61972106, U1636215, No.61871140), National Key research and Development Plan (Grant No. 2019QY1406, No. 2018YFB0803504), Guangdong Province Key research and Development Plan (Grant No. 2019B010136003 and No. 2019B010137004). Supported by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019).

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Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Weihong Han, Zhihong Tian & Shudong Li

  2. Computer School, National University of Defense Technology, Changsha, 410073, China

    Zizhong Huang

  3. Peng Cheng Laboratory, Cyberspace Security Research Center, Shenzhen, 518000, China

    Yan Jia

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  1. Weihong Han
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  2. Zhihong Tian
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Correspondence to Zhihong Tian.

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Han, W., Tian, Z., Huang, Z. et al. Topic representation model based on microblogging behavior analysis. World Wide Web 23, 3083–3097 (2020). https://doi.org/10.1007/s11280-020-00822-x

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