Dynamics of conflicts in Wikipedia (original) (raw)

The dynamic nature of conflict in Wikipedia

EPL (Europhysics Letters), 2014

The voluntary process of Wikipedia edition provides an environment where the outcome is clearly a collective product of interactions involving a large number of people. We propose a simple agent-based model, developed from real data, to reproduce the collaborative process of Wikipedia edition. With a small number of simple ingredients, our model mimics several interesting features of real human behaviour, namely in the context of edit wars. We show that the level of conflict is determined by a tolerance parameter, which measures the editors' capability to accept different opinions and to change their own opinion. We propose to measure conflict with a parameter based on mutual reverts, which increases only in contentious situations. Using this parameter, we find a distribution for the inter-peace periods that is heavy-tailed. The effects of wiki-robots in the conflict levels and in the edition patterns are also studied. Our findings are compared with previous parameters used to measure conflicts in edit wars.

Dynamics of Edit War Sequences in Wikipedia

2020

In any collaborative system, cooperation and conflicts exist together. While in some cases these conflicts improve the output, they also lead to increased overhead. This requires examining the dynamics of these conflicts with the help of underlying data. In Wikipedia articles, the conflicts are captured by edit wars which may be examined through the revision history of these articles. In this work, we perform a systematic analysis of the conflicts present in 1,208 controversial articles of Wikipedia captured in the form of edit war sequences. We examine various key characteristics of these sequences and further use them to estimate the outcome of the edit wars. The study indicates the possibility of devising automated coordination mechanisms for handling conflicts in collaborative spaces.

There is No Deadline - Time Evolution of Wikipedia Discussions

2012

Abstract: Wikipedia articles are by definition never finished. Many of them have associated talk pages, where their content is discussed by editors. Here we analyse the evolution of these discussions to unveil temporal patterns in the interactions in such a large production community. First, we investigate peaks in the discussion activity and their relation with peaks in edits to articles; furthermore we introduce a measure to account for how fast discussions grow in complexity.

Characterization and prediction of Wikipedia edit wars

2011

We present a new, efficient method for automatically detecting conflict cases and test it on five different language Wikipedias. We discuss how the number of edits, reverts, the length of discussions deviate in such pages from those following the general workflow.

Contropedia - the analysis and visualization of controversies in Wikipedia articles

Proceedings of The International Symposium on Open Collaboration - OpenSym '14, 2014

Collaborative content creation inevitably reaches situations where di↵erent points of view lead to conflict. In Wikipedia, one of the most prominent examples of collaboration online, conflict is mediated by both policy and software, and conflicts often reflect larger societal debates.

Controversy detection in Wikipedia using semantic dissimilarity

Information Sciences, 2017

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 nonexpert 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, we 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.

Controversy Detection in Wikipedia Using Collective Classification

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016

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. We hypothesize that intensities of controversy among related pages are not independent. Thus, we propose a stacked model which exploits the dependencies among related pages. Our 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.

On ranking controversies in Wikipedia: Models and evaluation

Proceedings of the …, 2008

Wikipedia 1 is a very large and successful Web 2.0 example. As the number of Wikipedia articles and contributors grows at a very fast pace, there are also increasing disputes occurring among the contributors. Disputes often happen in articles with controversial content. They also occur frequently among contributors who are "aggressive" or controversial in their personalities. In this paper, we aim to identify controversial articles in Wikipedia. We propose three models, namely the Basic model and two Controversy Rank (CR) models. These models draw clues from collaboration and edit history instead of interpreting the actual articles or edited content. While the Basic model only considers the amount of disputes within an article, the two Controversy Rank models extend the former by considering the relationships between articles and contributors. We also derived enhanced versions of these models by considering the age of articles. Our experiments on a collection of 19,456 Wikipedia articles shows that the Controversy Rank models can more effectively determine controversial articles compared to the Basic and other baseline models.