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Research:Topical coverage of Edit Wars

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Tracked in Phabricator:
Task T171249
Created
23:06, 16 September 2017 (UTC)
Duration:  2017-September – 2018-April
This page documents a completed research project.


This project is run by the Research Team as part of the Community health initiative. Here, we take a language agnostic approach, which focuses on edit wars, trying to differentiate between topic-centered and person-centered conflicts (edit wars).

The main outputs of this work are:

  • We found that around ~52% of the users are topic focused (they edit only in one topic), there are ~42% of the contributors that make big jumps across topics. Therefore, this can be described as **bimodal distribution**.
  • We have found that just 7% of edit wars are cross-topic.
  • However, users involved in this cross-topic edit wars are generally very active users, making difficult to assume that those wars are due person-centered conflicts.

Code (jupyter notebooks) and technical details about this project can be found in this repository

Introduction

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Background

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An edit war occurs when editors who disagree about the content of a page repeatedly override each other's contributions. When edit wars occur across multiple topics, they might be an indicator of a personal attack (instead of topic-centered) is occurring. This behavior might be categorized as wikihounding.

Proposal

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Edit wars on Wikipedia have been widely studied. We know they can be dectected in a content-agnostic (without analyzing the text) way. We propose studying the topical span of edit wars and characterizing usual and unusual (potentially toxic) behaviors.

The main tasks to develop such models are:

  • Define and implement a robust topic model.
    • Define a distance metric for topics (eg: Geography is N steps far from Politics, and M steps far from Sports.)
  • Generate a representative dataset of edit wars in Wikipedia.
  • Detect pairs or groups of users involved in more than X controversies. X will be defined as part of the study.
  • Apply an outlier detection mechanism to find potential cases of harassment.

Methodology

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  • Define a topic model that allows us to measure topic distance between Wikipedia pages.
  • Characterize a user's behavior according to the topics that the user edits and the amount of reverts the user commits.
  • Compute the probability of a pair of users co-revising a page and the probability that this co-revision is a revert.
  • Based on the aforementioned co-revision probability, identify anomalous behaviors that are potentially related to stalking or wikihounding behavior.

Topic Model

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  • The Topic Model described in this section is based on previous work on Wikiproject-based model developed here.

Mapping pages to topic

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Note

Topic distance

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  • Wikiprojects can be represented as a graph.
  • Given that each page can belong to more than one wikiproject, we define the distance between two pages as the minimum shortest path among all pairs of nodes on the Wikiprojects graph.
    • Example: Given a page X and Y, with X in Wikiprojects a and b and Y in Wikprojects c, and d. We compute the lenght of shortest path between (a,c), (a,d), (b,c) and (b,d), and return the minimum value among these results. In Python:
   def distancePages(Graph,page1,page2):
       """
       Graph: is the wikiprojects graphs
       return -2 if error  (page without wikiproject)
       return -1 if the two pages are the same
       else
       return shortest path
       """
       global pagesToWikiprojects
       results = []
       if page1==page2:
           return -1   
       try:
           pages1Projects = pagesToWikiprojects.get(page1,[])
           pages2Projects = pagesToWikiprojects.get(page2,[])
       except:
           return -2
       for x,y in product(pages1Projects,pages2Projects):
           try:
               results.append(nx.shortest_path_length(Graph,x,y))
           except:pass
       if not results:
           return -2
       else:
           return min(results)

User behavior

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Topical coverage

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  • Topical stability (us): For each user U, we obtain the distance for the user's next revisions.
  • For example, if user U makes three revisions, the first one in the topic 'Sports', the second one in the same topic, and the third one in the topic biology (with distance 4 from Sports), the probability that user U will edit with the topical distance of 0, is 2/3, with the topical distance of 4 is 1/3, and the probability is 0 for the all other distances. This metric gives an idea of user stability in terms of topics.

Reverting behavior

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  • For each user we compute a portion of reverts (within the dataset) compared with her/his total amount of revisions. Considering that our dataset contains only revisions by users who have made 10 or more revisions.

Note: We are just considering the reverts among these users.

Wikiprojects graph

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Terminology / conventions

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  • We use wikiprojects as proxies for topics.

Results

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Reverting behavior

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  • We found a strong correlation between the number of revisions and the number of reverts. This suggests that reverting is part of the task of active users.
  • Also, we found a change in the reverting behavior according to the date of user registration (account age/tenure), as expected, older users tend to be less reverted, and do more reverts.
% of Reverts according to account age/tenure (this is a Box plot)
% of Reverted according to account age/tenure (this is a Box plot)

A detailed analysis of reverting behavior can be found here: [2]


Characterization of user topic-focus

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  • Following our definition of topical stability, we see that 83.95% of 'next' revisions happen on the same page, and 99.25% on the same topic.
  • Moreover, 52.88% of users never jump out of the same topic. However, 41.88% of users jump more than 4 steps at least once.
Characterization Topic focus of Wikipedia Editors

More details can be found here: [3]

Characterization on topical distance in multipage editwars

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  • In order to have a notion of how frequent reverts and edit wars are across multiple topics, we considered all pairs of users with U, and V, where U has reverted V more than 2 times, and computed the topical distance between all the pairs of pages reverted.
  • Next, we computed the mode -- the most frequent value -- for each pair of users and reported the frequency of those values. As expected, most of wars, 71%, focus on one page and 22% are in the same topic but on a different page. The remaining 7% are cross-topic reverts. This reinforces our intuition that cross-topic edit wars are rare.
Characterization Topical Distance in Multipage Editwars
Distance %
-1 0.7107
0 0.2297
4 0.0143
5 0.0121
2 0.0103
3 0.0074
6 0.0056
7 0.0034
8 0.0027
1 0.0016
9 0.0012
10 0.0007
11 0.0002
12 0.0001


Activity and Editwars

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  • We found that almost 40% of users jump 4 or more steps in the Wikiprojects graph, making difficult to predict the likelihood of two users of co-editing the same page.
  • Moreover, we found that users involved in cross-topic edit wars tends also to be the more actives users, therefore we cannot assume that their behavior is related with person-centered problems or is just a consequence of their high activity.
Different types of Edit Warring behavior vs amount of activity

Details about this study can be found here: [4]

Conclusions and Main Outputs

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  • Based on previous work [5], we have implemented and released a model that allows us to measure topical distance between Wikipedia pages.
  • We have found that around 99% of 'next revisions' are done within the same topic.
  • We have found that just 7% of edit wars are cross-topic.
  • However, users involved in this cross-topic edit wars are generally very active users, making difficult to assume that those wars are due person-centered conflicts.

Resources

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Raw data

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Reverts dataset

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  • The interactions dataset can be downloaded here: [6]
   Format: interactions[user1][user2]:[[pageid,timestamp,deltatime,revision_id_reverted,revision_id_reverting], another revert, etc]
   user1: user reverting
   user2: user reverted
   pageid: page_id
   timestamp: timestamp when the reverted version was created (done by user2)
   deltatime: delta time from the reverted version to the reverting revision (done by user1).
   revision_id_reverted: revision_id_reverted (by user2)
   revision_id_reverting: revision_id_reverting (by user1)  

Wikiprojects graph

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Code

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  • Find all the code used in this study here:
    • Generate interactions dataset: [8]
    • Reverting Behavior study: [9]
    • Topic-Span of edit wars: [10]

See Also

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Acknowledgements

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https://meta.wikimedia.org/wiki/Research:Topical_coverage_of_Edit_Wars
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