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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Book ChapterDOI
Predicting 2016 US Presidential Election Polls with Online and Media Variables
TL;DR: The results suggest that machine learning models with linear regression can produce quite accurate predictions; also statistically significant correlations were found between polls and betting odds and polls and Facebook page likes.
Book ChapterDOI
Adding Taxonomies Obtained by Content Clustering to Semantic Social Network Analysis
TL;DR: A novel method to analyze the content of communication in social networks and extracts a taxonomy of concepts based on terms extracted from the communication’s content to provide insights not possible through conventional social network analysis.
Book ChapterDOI
Identifying Tribes on Twitter Through Shared Context
Peter A. Gloor,Andrea Fronzetti Colladon,Joao Marcos de Oliveira,Paola Rovelli,Manuel Galbier,Manfred Vogel +5 more
TL;DR: Tribefinder is introduced, a novel system able to reveal Twitter users’ tribal affiliations through the analysis of their tweets and the comparison of their vocabulary, and the results of the adoption of a t-SNE visualization approach, useful to verify whether tribe members cluster closely together.
Journal ArticleDOI
Recognizing Communication Patterns in Chronic Care Innovation Networks
TL;DR: The preliminary findings show that high-performing teams tend to interact in less close-knit networks characterized by lower network density and a more balanced exchange of information among team members.
Journal ArticleDOI
Analyzing the Flow of Knowledge with Sociometric Badges
Kai Fischbach,Peter A. Gloor,Casper Lassenius,Daniel Olguin Olguin,Alex Pentland,Johannes Putzke,Detlef Schoder +6 more
TL;DR: In this paper, the authors present a collection of best practices for the use of sociometric badges that support automatic collection of face-to-face interaction between workers within an organization.