Elena Simperl

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Elena Simperl 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
Identifying, understanding and detecting recurring, harmful behavior patterns in collaborative wikipedia editing - Doctoral proposal Collaboration systems
Collective intelligence
Editing behavior
Social dynamics
User modeling
Web science
Wikipedia
WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web English 2013 In this doctoral proposal, we describe an approach to identify recurring, collective behavioral mechanisms in the collaborative interactions of Wikipedia editors that have the potential to undermine the ideals of quality, neutrality and completeness of article content. We outline how we plan to parametrize these patterns in order to understand their emergence and evolution and measure their effective impact on content production in Wikipedia. On top of these results we intend to build end-user tools to increase the transparency of the evolution of articles and equip editors with more elaborated quality monitors. We also sketch out our evaluation plans and report on already accomplished tasks. 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
Reverts revisited - Accurate revert detection in wikipedia Collaboration systems
Community-driven content creation
Editing behavior
Revert detection
Social dynamics
User modeling
Wikipedia
HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media English 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 content 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. Copyright 2012 ACM. 0 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
Enterprise Wikis: Technical challenges and opportunities Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) English 2011 Social software has proven valuable in enterprises for collaborative knowledge management. In order to introduce a wiki in the enterprise, we propose a solution that combinesWeb 2.0 and SemanticWeb technologies. We describe how this solution resolves the technical challenges, beyond that, opens up new opportunities, and, also, how it can be realized in a concrete enterprise scenario. 0 0
Wikiing pro: semantic wiki-based process editor English 2011 Recently, a trend toward collaborative, user-centric, on-line process modeling can be observed. Unfortunately, current social software approaches mostly focus on the graphical development of processes and do not consider existing textual process description like HowTos or guidelines. We address this issue by combining graphical process modeling techniques with a wiki-based light-weight knowledge capturing approach and a background semantic knowledge base. Our approach enables the collaborative maturing of process descriptions with a graphical representation, formal semantic annotations, and natural language. By translating existing textual process descriptions into graphical descriptions and formal semantic annotations, we provide a holistic approach for collaborative process development that is designed to foster knowledge reuse and maturing within the system. 0 0
Overcoming information overload in the enterprise: The active approach Context mining
Information overload
Internet
Knowledge management
Knowledge process
Knowledge worker
Productivity
Semantic wiki
IEEE Internet Computing English 2010 Knowledge workers are central to an organization's success, yet their information management tools often hamper their productivity. This has major implications for businesses across the globe because their commercial advantage relies on the optimal exploitation of their own enterprise information, the huge volumes of online information, and the productivity of the required knowledge work. The Active project addresses this challenge through an integrated knowledge management workspace that reduces information overload by significantly improving the mechanisms for creating, managing, and using information. The project's approach follows three themes: sharing information through tagging, wikis, and ontologies; prioritizing information delivery by understanding users' current-task context; and leveraging informal processes that are learned from user behavior. 0 0