Jurgens and Lu ICWSM 2012

Citation

@inproceedings{DBLP:conf/icwsm/JurgensL12,

 author = {David Jurgens and Tsai-Ching Lu},
 title = {Temporal Motifs Reveal the Dynamics of Editor Interactions in Wikipedia},
 booktitle = {ICWSM},
 year = {2012}

Online version

Temporal Motifs Reveal the Dynamics of Editor Interactions in Wikipedia

Summary

Underlying the growth of Wikipedia are the cooperative –and sometimes combative– interactions between editors working on the same content. But most research on Wikipedia editor interactions focus on cooperative behaviors, which calls for a full analysis of all types of editing behaviors, including both cooperative and combative. To investigate editor interactions in Wikipedia in this context, this paper proposes to represent Wikipedia's revision history as a temporal, bipartite network with multiple node and edge types for users and revisions. From this representation, they identify author interactions as network motifs and show how the motif types capture editing behaviors. They demonstrate the usefulness of motifs by two tasks; (1) classification of pages as combative or cooperative page and (2) analysis of the dynamics of editor behavior to explain Wikipedia’s content growth.

Proposed analysis method

Network representation

They view editor interactions in Wikipedia as a bipartite graph from authors to the pages. They expand this representation to encode three additional features: (1) the type of author who made the change, (2) the time at which the change was made, and (3) the magnitude and effect of the change to the page. To do so, they define the bipartite graph of Wikipedia revisions as follows.

The figure below illustrates a subset of a page’s history as sequence of classified revisions.

Network derivation from Wikipedia dataset

Data:

Wikipedia revision dataset is derived from a complete revision history of Wikipedia, ending on April 05, 2011.
After extracting article pages that have at least 10 revisions, the resulting dataset contained 2,715,123 articles and 227,034,806 revisions.

Revision classes:

They selected four high-level categories for revisions: adding, deleting, editing, and reverting.
Using (1) the revising author’s comment and (2) MD5 hash for the articles, a revision can be identified as revert or not.
To classify a revision into one of the other three revision classes, they used two parameters: (1) the number of whitespace-delimited tokens added or removed from the page, $\delta$ , i.e., its change in size, and (2) the number of tokens whose content was changed, $\theta$ .
The classification rule is as follows.

To further distinguish edits based on the magnitude of their effect in addition to the type, they partition each class into major and minor subcategories, with the exception of Revert.
Based on the shape of the effect distributions, the difference between major and minor was selected using the Pareto principle, or “80/20 rule” (Newman, M. 2005. Power laws, pareto distributions and zipf’s law. Contemporary physics 46(5):323–351.).
The intuition here is, the revisions with small effects account for the majority of the cumulative effects to the content.
The figure belos shows distributions of the effects for Add, Delete, and Edit types. Vertical lines indicate the division between major and minor revisions based on the 80/20 rule, where 80% of a type’s cumulative effects are due to those to the left of the line.

Network motifs

The set of candidate motifs was selected from all subgraphs made of three contiguous edits on a single page.

Demonstration of the usefulness of the motifs

Classification of pages as combative or cooperative page

Identifying cooperative/combative pages:

To identifying cooperative/combative pages, they used established categories of pages. Combative pages are 720 pages listed in Wikipedia:List of Controversial Articles, and cooperative pages is 10,149 pages in Wikipedia:Good Articles and Wikipedia:Featured articles, with the assumption that high quality pages will have more cooperative interactions. Other pages are classified into neutral pages.

Experimental setting:

The classification algorithm used here is SVM. They compared the result when motifs were used as features to the result when author-edit types were used as features. As a classification performance measure, they used F-scores for each page class. When using motifs as features, they used only the $k$ most frequent motif types, varying the value of $k$ .

Result:

The table below shows F-scores for each page class. It shows that the motifs features contribute the increase of classification accuracy, with enough amount of motifs, especially for the classification of combative/cooperative pages.

Analysis of content growth

Review

Recommendation for whether or not to assign the paper as required/optional reading in later classes.

No. The method used here is very simple, so if someone gets interested in this topic, it may be enough to look at this summary.

Strengths and weaknesses

Strength

Weakness

Possible impact

Related Papers

Study Plan

Wikipedia article on LDA
Blei, D.; Ng, A.; and Jordan, M. 2003. Latent dirichlet allocation. The Journal of Machine Learning Research 3:993– 1022. (Original paper on LDA)