NDLI: WikiWho: precise and efficient attribution of authorship of revisioned content

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WikiWho: precise and efficient attribution of authorship of revisioned content

Content Provider	ACM Digital Library
Author	Flöck, Fabian Acosta, Maribel
Abstract	Revisioned text content is present in numerous collaboration platforms on the Web, most notably Wikis. To track authorship of text tokens in such systems has many potential applications; the identification of main authors for licensing reasons or tracing collaborative writing patterns over time, to name some. In this context, two main challenges arise. First, it is critical for such an authorship tracking system to be precise in its attributions, to be reliable for further processing. Second, it has to run efficiently even on very large datasets, such as Wikipedia. As a solution, we propose a graph-based model to represent revisioned content and an algorithm over this model that tackles both issues effectively. We describe the optimal implementation and design choices when tuning it to a Wiki environment. We further present a gold standard of 240 tokens from English Wikipedia articles annotated with their origin. This gold standard was created manually and confirmed by multiple independent users of a crowdsourcing platform. It is the first gold standard of this kind and quality and our solution achieves an average of 95% precision on this data set. We also perform a first-ever precision evaluation of the state-of-the-art algorithm for the task, exceeding it by over 10% on average. Our approach outperforms the execution time of the state-of-the-art by one order of magnitude, as we demonstrate on a sample of over 240 English Wikipedia articles. We argue that the increased size of an optional materialization of our results by about 10% compared to the baseline is a favorable trade-off, given the large advantage in runtime performance.
Starting Page	843
Ending Page	854
Page Count	12
File Format	PDF
ISBN	9781450327442
DOI	10.1145/2566486.2568026
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-04-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Online collaboration Version control Authorship Community-driven content creation Content modeling Collaborative writing Wikipedia
Content Type	Text
Resource Type	Article

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