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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.

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Journal ArticleDOI

Understanding the effect of social networks on user behaviors in community-driven knowledge services

TL;DR: Examination of the influence of the structural and relational attributes of social networks on the quality of answers at CKSs for answering ties, co-answering ties, and getting answers ties shows that the centrality of the answering ties significantly influences thequality of answers while the average strength of the answered ties has an insignificant effect on theQuality of answers.
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Analyzing Cultural Differences in Collaborative Innovation Networks by Analyzing Editing Behavior in Different-Language Wikipedias

TL;DR: This article analyzed the process of article creation and communal interaction in different language Wikipedias through the lens of social network analysis and identified cultural differences in online collaborative innovation networks by comparing the English, German, Japanese, Korean, and Finish language Wikipedia.
Book ChapterDOI

Muse Headband: Measuring Tool or a Collaborative Gadget?

TL;DR: It is found that the usefulness in measuring EEG signal of consumer-grade devices such as Muse is extremely limited in non-laboratory conditions.
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The Power of Alumni Networks - Success of Startup Companies Correlates With Online Social Network Structure of Its Founders

TL;DR: In this paper, the authors analyzed the success of startups in Germany by looking at the social network structure of their founders on the German-language business-networking site XING and found that universities which are more central in the German university network, provide a better environment for students to found more and more successful startups.
Journal ArticleDOI

Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage

TL;DR: Tribefinder is presented, a system to reveal Twitter users’ tribal affiliations, by analyzing their tweets and language use, and illustrates the importance of adopting a new lens for studying virtual tribes, crucial for firms to properly design their marketing strategy, and for scholars to extend prior marketing research.