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Discovering story chains: A framework based on zigzagged search and news actors

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  • Cagri Toraman
  • Fazli Can

Abstract

A story chain is a set of related news articles that reveal how different events are connected. This study presents a framework for discovering story chains, given an input document, in a text collection. The framework has 3 complementary parts that i) scan the collection, ii) measure the similarity between chain‐member candidates and the chain, and iii) measure similarity among news articles. For scanning, we apply a novel text‐mining method that uses a zigzagged search that reinvestigates past documents based on the updated chain. We also utilize social networks of news actors to reveal connections among news articles. We conduct 2 user studies in terms of 4 effectiveness measures—relevance, coverage, coherence, and ability to disclose relations. The first user study compares several versions of the framework, by varying parameters, to set a guideline for use. The second compares the framework with 3 baselines. The results show that our method provides statistically significant improvement in effectiveness in 61% of pairwise comparisons, with medium or large effect size; in the remainder, none of the baselines significantly outperforms our method.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jinfst:v:68:y:2017:i:12:p:2795-2808
    DOI: 10.1002/asi.23885
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    References listed on IDEAS

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    1. 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.
    2. Fazli Can & Seyit Kocberber & Ozgur Baglioglu & Suleyman Kardas & H. Cagdas Ocalan & Erkan Uyar, 2010. "New event detection and topic tracking in Turkish," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(4), pages 802-819, April.
    3. Fazli Can & Seyit Kocberber & Erman Balcik & Cihan Kaynak & H. Cagdas Ocalan & Onur M. Vursavas, 2008. "Information retrieval on Turkish texts," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(3), pages 407-421, February.
    4. Fazli Can & Seyit Kocberber & Ozgur Baglioglu & Suleyman Kardas & H. Cagdas Ocalan & Erkan Uyar, 2010. "New event detection and topic tracking in Turkish," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(4), pages 802-819, April.
    5. Jun Song & Yu Huang & Xiang Qi & Yuheng Li & Feng Li & Kun Fu & Tinglei Huang, 2016. "Discovering hierarchical topic evolution in time-stamped documents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(4), pages 915-927, April.
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