Discovering Story Chains: A Framework Based on Zigzagged Search and News Actors

Date

2017-12-01

Author

Toraman, Çağrı
Can, Fazli

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

URI

https://hdl.handle.net/11511/109672

Collections

Department of Computer Engineering, Article