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

Cagri Toraman and Fazli Can

Journal of the Association for Information Science & Technology, 2017, vol. 68, issue 12, 2795-2808

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.

Date: 2017
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