P
Peter A. Gloor
Researcher at Massachusetts Institute of Technology
Publications - 230
Citations - 5644
Peter A. Gloor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Social network analysis & Social network. The author has an hindex of 37, co-authored 211 publications receiving 4918 citations. Previous affiliations of Peter A. Gloor include University of Cologne & Union Bank of Switzerland.
Papers
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Proceedings Article
Capturing Individual and Group Behavior with Wearable Sensors
TL;DR: It is shown how to obtain high level descriptions of human behavior in terms of physical activity, speech activity, face-to-face interaction, physical proximity, and social network attributes from sensor data.
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In bot we trust: A new methodology of chatbot performance measures
Aleksandra Przegalinska,Leon Ciechanowski,Leon Ciechanowski,Anna Stróż,Peter A. Gloor,Grzegorz Mazurek +5 more
TL;DR: A novel method of analyzing the content of messages produced in human-chatbot interactions is proposed, using the Condor Tribefinder system the authors developed for text mining that is based on a machine learning classification engine.
Proceedings ArticleDOI
Web Science 2.0: Identifying Trends through Semantic Social Network Analysis
TL;DR: A novel set of social network analysis based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new trends to predict long-term trends on the popularity of relevant concepts such as brands, movies, and politicians are introduced.
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Correlating temporal communication patterns of the Eclipse open source community with performance and creativity
Yared H. Kidane,Peter A. Gloor +1 more
TL;DR: Preliminary results indicate that there is a correlation between attributes of social networks such as density and betweenness centrality and group productivity measures in an open source development community.
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Finding Collaborative Innovation Networks Through Correlating Performance with Social Network Structure
TL;DR: This research compares social network structure and individual and team performance of participants in a multi-user online computer game with social network structures and performance among the student teams, and suggests a balanced contribution index predicts performance of the student knowledge worker teams.