Fabian Flöck

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Fabian Flöck is an author from Germany.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
WikiWho: Precise and Efficient Attribution of Authorship of Revisioned Content Wikipedia
Version control
Content modeling
Community- driven content creation
Collaborative authoring
Online collaboration
Authorship
World Wide Web Conference 2014 English 2014 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. 0 0
Reverts Revisited: Accurate Revert Detection in Wikipedia Wikipedia
Revert detection
Editing behavior
User modeling
Collaboration systems
Community-driven content creation
Social dynamics
Hypertext and Social Media 2012 English June 2012 Wikipedia is commonly used as a proving ground for research in collaborative systems. This is likely due to its popularity and scale, but also to the fact that large amounts of data about its formation and evolution are freely available to inform and validate theories and models of online collaboration. As part of the development of such approaches, revert detection is often performed as an important pre-processing step in tasks as diverse as the extraction of implicit networks of editors, the analysis of edit or editor features and the removal of noise when analyzing the emergence of the con-tent of an article. The current state of the art in revert detection is based on a rather naïve approach, which identifies revision duplicates based on MD5 hash values. This is an efficient, but not very precise technique that forms the basis for the majority of research based on revert relations in Wikipedia. In this paper we prove that this method has a number of important drawbacks - it only detects a limited number of reverts, while simultaneously misclassifying too many edits as reverts, and not distinguishing between complete and partial reverts. This is very likely to hamper the accurate interpretation of the findings of revert-related research. We introduce an improved algorithm for the detection of reverts based on word tokens added or deleted to adresses these drawbacks. We report on the results of a user study and other tests demonstrating the considerable gains in accuracy and coverage by our method, and argue for a positive trade-off, in certain research scenarios, between these improvements and our algorithm’s increased runtime. 13 0
Towards a diversity-minded Wikipedia Wikipedia
Diversity
Community-driven content creation
Social dynamics
Opinion mining
Sentiment analysis
WebSci Conference English June 2011 Wikipedia is a top-ten Web site providing a free encyclopedia created by an open community of volunteer contributors. As investigated in various studies over the past years, contributors have different backgrounds, mindsets and biases; however, the effects - positive and negative - of this diversity on the quality of the Wikipedia content, and on the sustainability of the overall project are yet only partially understood. In this paper we discuss these effects through an analysis of existing scholarly literature in the area and identify directions for future research and development; we also present an approach for diversity-minded content management within Wikipedia that combines techniques from semantic technologies, data and text mining and quantitative social dynamics analysis to create greater awareness of diversity-related issues within theWikipedia community, give readers access to indicators and metrics to understand biases and their impact on the quality of Wikipedia articles, and support editors in achieving balanced versions of these articles that leverage the wealth of knowledge and perspectives inherent to large-scale collaboration. 24 1