Characterization and prediction of Wikipedia edit wars (original) (raw)
Related papers
2011
Abstract We present a new, efficient method for automatically detecting severe conflicts,edit wars' in Wikipedia and evaluate this method on six different language Wikipedias. We discuss how the number of edits and reverts deviate in such pages from those following the general workflow, and argue that earlier work has significantly over-estimated the contentiousness of the Wikipedia editing process.
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.
USING N-GRAMS TO IDENTIFY EDIT WARS ON WIKIPEDIA
2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), Singapore, Singapore, 2019, 2019
This paper presents the method of identifying Wikipedia edit wars using N-grams analysis. The analysis is conducted on the corpus of past versions of Wikipedia pages concerning historical figures who are glorified and idolised by the Hindu Right. The analysis shows that Wikipedia's open structure and Article Policies enable a conversation between academic and popular histories, a feat which has been difficult in India in the past.
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.
An Agent-Based Model of Edit Wars in Wikipedia: How and When Consensus is Reached
Edit wars are conflicts among editors of Wikipedia when editors repeatedly overwrite each other's content. Edit wars can last from a few days to several years before reaching consensus often leading to a loss of content quality. Therefore, the goal of this paper is to create an agent-based model of edit wars in order to study the influence of various factors involved in consensus formation. We model the behavior of agents using theories of group stability and reinforcement learning. We show that increasing the number of credible or trustworthy agents and agents with a neutral point of view decreases the time taken to reach consensus, whereas the duration is longest when agents with opposing views are in equal proportion. Our model can be used to study the behavior of members in online communities and to inform policies and guidelines for participation.
Dynamics of conflicts in Wikipedia
2012
In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies.
Finding Structure in Wikipedia Edit Activity
Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion
This paper documents a study of the real-time Wikipedia edit stream containing over 6 million edits on 1.5 million English Wikipedia articles, during 2015. We focus on answering questions related to identification and use of information cascades between Wikipedia articles, based on author editing activity. Our findings show that by constructing information cascades between Wikipedia articles using editing activity, we are able to construct an alternative linking structure in comparison to the embedded links within a Wikipedia page. This alternative article hyperlink structure was found to be relevant in topic, and timely in relation to external global events (e.g., political activity). Based on our analysis, we contextualise the findings against areas of interest such as events detection, vandalism, edit wars, and editing behaviour.
Physical Review E, 2015
A model for the probabilistic function followed in editing Wikipedia is presented and compared with simulations and real data. It is argued that the probability of editing is proportional to the editor's number of previous edits (preferential attachment), to the editor's fitness, and to an aging factor. Using these simple ingredients, it is possible to reproduce the results obtained for Wikipedia editing dynamics for a collection of single pages as well as the averaged results. Using a stochastic process framework, a recursive equation was obtained for the average of the number of edits per editor that seems to describe the editing behavior in Wikipedia.
Temporal Motifs Reveal the Dynamics of Editor Interactions in Wikipedia
2012
Wikipedia is a collaborative setting with both combative and cooperative editing. We propose a new method for investigating the types of editor interactions using a novel representation of Wikipedia's revision history as a temporal, bipartite network with multiple node and edge types for users and revisions. From this representation we identify significant author interactions as network motifs and show how the motif types capture important, diverse editing behaviors. Two experiments demonstrate the further benefit of motifs. First, we demonstrate significant performance improvement over a purely revision-based analysis in classifying pages as combative or cooperative page by using motifs; and second we use motifs as a basis for analyzing trends in the dynamics of editor behavior to explain Wikipedia's content growth.