Difference between revisions of "Controversy Detection in Wikipedia Using Collective Classification"
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| − | '''Controversy Detection in Wikipedia Using Collective Classification''' - scientific work related to Wikipedia quality published in 2016, written by Shiri Dori-Hacohen, David D. Jensen and James Allan. | + | '''Controversy Detection in Wikipedia Using Collective Classification''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Shiri Dori-Hacohen]], [[David D. Jensen]] and [[James Allan]]. |
== Overview == | == Overview == | ||
| − | Concerns over personalization in IR have sparked an interest in detection and analysis of controversial topics. Accurate detection would enable many beneficial applications, such as alerting search users to controversy. Wikipedia's broad coverage and rich metadata offer a valuable resource for this problem. Authors hypothesize that intensities of controversy among related pages are not independent; thus, authors propose a stacked model which exploits the dependencies among related pages. Authors approach improves classification of controversial web pages when compared to a model that examines each page in isolation, demonstrating that controversial topics exhibit homophily. Using notions of similarity to construct a subnetwork for collective classification, rather than using the default network present in the relational data, leads to improved classification with wider applications for semi-structured datasets, with the effects most pronounced when a small set of neighbors is used. | + | Concerns over personalization in IR have sparked an interest in detection and analysis of controversial topics. Accurate detection would enable many beneficial applications, such as alerting search users to controversy. [[Wikipedia]]'s broad coverage and rich metadata offer a valuable resource for this problem. Authors hypothesize that intensities of controversy among related pages are not independent; thus, authors propose a stacked model which exploits the dependencies among related pages. Authors approach improves classification of controversial web pages when compared to a model that examines each page in isolation, demonstrating that controversial topics exhibit homophily. Using notions of similarity to construct a subnetwork for collective classification, rather than using the default network present in the relational data, leads to improved classification with wider applications for semi-structured datasets, with the effects most pronounced when a small set of neighbors is used. |
Revision as of 08:44, 30 April 2020
Controversy Detection in Wikipedia Using Collective Classification - scientific work related to Wikipedia quality published in 2016, written by Shiri Dori-Hacohen, David D. Jensen and James Allan.
Overview
Concerns over personalization in IR have sparked an interest in detection and analysis of controversial topics. Accurate detection would enable many beneficial applications, such as alerting search users to controversy. Wikipedia's broad coverage and rich metadata offer a valuable resource for this problem. Authors hypothesize that intensities of controversy among related pages are not independent; thus, authors propose a stacked model which exploits the dependencies among related pages. Authors approach improves classification of controversial web pages when compared to a model that examines each page in isolation, demonstrating that controversial topics exhibit homophily. Using notions of similarity to construct a subnetwork for collective classification, rather than using the default network present in the relational data, leads to improved classification with wider applications for semi-structured datasets, with the effects most pronounced when a small set of neighbors is used.
