Computing controversy: Formal model and algorithms for detecting controversy on Wikipedia and in search queries. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Zielinski, Kazimierz; Nielek, Radoslaw; Wierzbicki, Adam; Jatowt, Adam
• Is Part Of: Information processing & management. Volume 54:Issue 1(2018:Jan.)
• Page Start: 14
• Page End: 36
• Publication Date: 2018-01
• Subjects: Controversy -- Wikipedia -- Web search; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2017.08.005
• Languages: English
• ISSNs: 0306-4573

Abstract

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.