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Title: Computing controversy: Formal model and algorithms for detecting controversy on Wikipedia and in search queries
Authors: Zielinski, Kazimierz
Nielek, Radoslaw
Wierzbicki, Adam
Jatowt, Adam  kyouindb  KAKEN_id
Author's alias: Jatowt, Adam-wladyslaw
Keywords: Controversy
Web search
Issue Date: Jan-2018
Publisher: Elsevier BV
Journal title: Information Processing & Management
Volume: 54
Issue: 1
Start page: 14
End page: 36
Abstract: Controversy is a complex concept that has been attracting attention of scholars from diverse fields. In the era of Internet and social media, detecting controversy and controversial concepts by the means of automatic methods is especially important. Web searchers could be alerted when the contents they consume are controversial or when they attempt to acquire information on disputed topics. Presenting users with the indications and explanations of the controversy should offer them chance to see the “wider picture” rather than letting them obtain one-sided views. In this work we first introduce a formal model of controversy as the basis of computational approaches to detecting controversial concepts. Then we propose a classification based method for automatic detection of controversial articles and categories in Wikipedia. Next, we demonstrate how to use the obtained results for the estimation of the controversy level of search queries. The proposed method can be incorporated into search engines as a component responsible for detection of queries related to controversial topics. The method is independent of the search engine’s retrieval and search results recommendation algorithms, and is therefore unaffected by a possible filter bubble. Our approach can be also applied in Wikipedia or other knowledge bases for supporting the detection of controversy and content maintenance. Finally, we believe that our results could be useful for social science researchers for understanding the complex nature of controversy and in fostering their studies.
Rights: © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. (
DOI(Published Version): 10.1016/j.ipm.2017.08.005
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