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Discovering story chains: A framework based on zigzagged search and news actors
Author(s) -
Toraman Cagri,
Can Fazli
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23885
Subject(s) - computer science , information retrieval , pairwise comparison , similarity (geometry) , set (abstract data type) , relevance (law) , coherence (philosophical gambling strategy) , measure (data warehouse) , similarity measure , data mining , image (mathematics) , artificial intelligence , statistics , mathematics , political science , law , programming language
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.