Difference between revisions of "Controversy Detection in Wikipedia Using Semantic Dissimilarity"

From Wikipedia Quality
Jump to: navigation, search
(+ embed code)
(Category)
 
Line 32: Line 32:
 
</nowiki>
 
</nowiki>
 
</code>
 
</code>
 +
 +
 +
 +
[[Category:Scientific works]]

Latest revision as of 00:35, 6 January 2021


Controversy Detection in Wikipedia Using Semantic Dissimilarity
Authors
M. Zeeshan Jhandir
Ali Tenvir
Byung-Won On
Ingyu Lee
Gyu Sang Choi
Publication date
2017
DOI
10.1016/j.ins.2017.08.037
Links
Original

Controversy Detection in Wikipedia Using Semantic Dissimilarity - scientific work related to Wikipedia quality published in 2017, written by M. Zeeshan Jhandir, Ali Tenvir, Byung-Won On, Ingyu Lee and Gyu Sang Choi.

Overview

Abstract The advent of search engines and wikis has made access to information easy and almost free. Wikipedia is the efficacious outcome of an enormous collaboration, and its peer review-like methods of creation, maintenance, and evolution of contents, ensure high quality and reliability. However, the “anyone-can-edit” policy of Wikipedia has created many problems such as trolling, vandalism, controversies, and doubts about the content and reliability of the information provided due to non-expert involvement. People have tried to identify and rank controversies in Wikipedia articles through various techniques that use quantitative data, ignoring the semantic significance of conflicts among authors. In this paper, authors have addressed the problem of identifying controversy using natural language processing techniques for the first time. The proposed method spots the impact on existing meanings of the text due to new editing processes along with their relationship to the topic of the article. The experimental results for precision (0.901), recall (0.901), accuracy (0.908), and F-measure (0.901) demonstrate the effectiveness of the proposed method. The technique is deemed useful for automatic identification of conflicts newly introduced into existing article contents, and could prove helpful in making decisions for inclusion or exclusion of controversies under the same topic.

Embed

Wikipedia Quality

Jhandir, M. Zeeshan; Tenvir, Ali; On, Byung-Won; Lee, Ingyu; Choi, Gyu Sang. (2017). "[[Controversy Detection in Wikipedia Using Semantic Dissimilarity]]". Elsevier. DOI: 10.1016/j.ins.2017.08.037.

English Wikipedia

{{cite journal |last1=Jhandir |first1=M. Zeeshan |last2=Tenvir |first2=Ali |last3=On |first3=Byung-Won |last4=Lee |first4=Ingyu |last5=Choi |first5=Gyu Sang |title=Controversy Detection in Wikipedia Using Semantic Dissimilarity |date=2017 |doi=10.1016/j.ins.2017.08.037 |url=https://wikipediaquality.com/wiki/Controversy_Detection_in_Wikipedia_Using_Semantic_Dissimilarity |journal=Elsevier}}

HTML

Jhandir, M. Zeeshan; Tenvir, Ali; On, Byung-Won; Lee, Ingyu; Choi, Gyu Sang. (2017). &quot;<a href="https://wikipediaquality.com/wiki/Controversy_Detection_in_Wikipedia_Using_Semantic_Dissimilarity">Controversy Detection in Wikipedia Using Semantic Dissimilarity</a>&quot;. Elsevier. DOI: 10.1016/j.ins.2017.08.037.