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Finding story chains in newswire articles using random walks

Author

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  • Xianshu Zhu

    (University of Maryland, Baltimore County)

  • Tim Oates

    (University of Maryland, Baltimore County)

Abstract

Massive amounts of information about news events are published on the Internet every day in online newspapers, blogs, and social network messages. While search engines like Google help retrieve information using keywords, the large volumes of unstructured search results returned by search engines make it hard to track the evolution of an event. A story chain is composed of a set of news articles that reveal hidden relationships among different events. Traditional keyword-based search engines provide limited support for finding story chains. In this paper, we propose a random walk based algorithm to find story chains. When breaking news happens, many media outlets report the same event. We have two pruning mechanisms in the algorithm to automatically exclude redundant articles from the story chain and to ensure efficiency of the algorithm. We further explore how named entities and word relevance can help find relevant news articles and improve algorithm efficiency by creating a co-clustering based correlation graph. Experimental results show that our proposed algorithm can generate coherent story chains without redundancy. The efficiency of the algorithm is significantly improved on the correlation graph.

Suggested Citation

  • Xianshu Zhu & Tim Oates, 2014. "Finding story chains in newswire articles using random walks," Information Systems Frontiers, Springer, vol. 16(5), pages 753-769, November.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:5:d:10.1007_s10796-013-9420-2
    DOI: 10.1007/s10796-013-9420-2
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    Cited by:

    1. Jong Hwan Suh, 2019. "SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques," Sustainability, MDPI, vol. 11(1), pages 1-44, January.
    2. Cagri Toraman & Fazli Can, 2017. "Discovering story chains: A framework based on zigzagged search and news actors," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2795-2808, December.
    3. Chengcui Zhang & Elisa Bertino & Bhavani Thuraisingham & James Joshi, 2014. "Guest editorial: Information reuse, integration, and reusable systems," Information Systems Frontiers, Springer, vol. 16(5), pages 749-752, November.

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