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Jeffrey Rzeszotarski


Publications by Jeffrey Rzeszotarski (bibliography)

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Rzeszotarski, Jeffrey and Kittur, Aniket (2012): Learning from history: predicting reverted work at the word level in wikipedia. In: Proceedings of ACM CSCW12 Conference on Computer-Supported Cooperative Work 2012. pp. 437-440. Available online

Wikipedia's remarkable success in aggregating millions of contributions can pose a challenge for current editors, whose hard work may be reverted unless they understand and follow established norms, policies, and decisions and avoid contentious or proscribed terms. We present a machine learning model for predicting whether a contribution will be reverted based on word level features. Unlike previous models relying on editor-level characteristics, our model can make accurate predictions based only on the words a contribution changes. A key advantage of the model is that it can provide feedback on not only whether a contribution is likely to be rejected, but also the particular words that are likely to be controversial, enabling new forms of intelligent interfaces and visualizations. We examine the performance of the model across a variety of Wikipedia articles.

© All rights reserved Rzeszotarski and Kittur and/or ACM Press

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Rzeszotarski, Jeffrey and Kittur, Aniket (2012): CrowdScape: interactively visualizing user behavior and output. In: Proceedings of the 2012 ACM Symposium on User Interface Software and Technology 2012. pp. 55-62. Available online

Crowdsourcing has become a powerful paradigm for accomplishing work quickly and at scale, but involves significant challenges in quality control. Researchers have developed algorithmic quality control approaches based on either worker outputs (such as gold standards or worker agreement) or worker behavior (such as task fingerprinting), but each approach has serious limitations, especially for complex or creative work. Human evaluation addresses these limitations but does not scale well with increasing numbers of workers. We present CrowdScape, a system that supports the human evaluation of complex crowd work through interactive visualization and mixed initiative machine learning. The system combines information about worker behavior with worker outputs, helping users to better understand and harness the crowd. We describe the system and discuss its utility through grounded case studies. We explore other contexts where CrowdScape's visualizations might be useful, such as in user studies.

© All rights reserved Rzeszotarski and Kittur and/or ACM Press

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