Finding patterns in behavioral observations by automatically labeling forms of wikiwork in Barnstars
|Finding patterns in behavioral observations by automatically labeling forms of wikiwork in Barnstars|
|Author(s)||McDonald D.W., Javanmardi S., Zachry M.|
|Published in||WikiSym 2011 Conference Proceedings - 7th Annual International Symposium on Wikis and Open Collaboration|
|Keyword(s)||behavioral patterns, multi-label learning, Wikipedia (Extra: Behavioral observation, Behavioral patterns, Data sets, Multi-label, Online communities, Random forests, Sense making, Social media, Training and testing, Unlabeled data, Wikipedia, Decision trees, Data processing)|
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Finding patterns in behavioral observations by automatically labeling forms of wikiwork in Barnstars is a 2011 conference paper written in English by McDonald D.W., Javanmardi S., Zachry M. and published in WikiSym 2011 Conference Proceedings - 7th Annual International Symposium on Wikis and Open Collaboration.
Our everyday observations about the behaviors of others around us shape how we decide to act or interact. In social media the ability to observe and interpret others' behavior is limited. This work describes one approach to leverage everyday behavioral observations to develop tools that could improve understanding and sense making capabilities of contributors, managers and researchers of social media systems. One example of behavioral observation is Wikipedia Barnstars. Barnstars are a type of award recognizing the activities of Wikipedia editors. We mine the entire English Wikipedia to extract barnstar observations. We develop a multi-label classifier based on a random forest technique to recognize and label distinct forms of observed and acknowledged activity. We evaluate the classifier through several means including use of separate training and testing datasets and the by application of the classifier to previously unlabeled data. We use the classifier to identify Wikipedia editors who have been observed with some predominant types of behavior and explore whether those patterns of behavior are evident and how observers seem to be making the observations. We discuss how these types of activity observations can be used to develop tools and potentially improve understanding and analysis in wikis and other online communities.
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