Peter A. Gloor

Bio: 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.

Predicting Client Satisfaction through (E-Mail) Network Analysis: The Communication Score Card

Abstract: This study seeks to better understand the network characteristics of client support teams by analyzing the teams' e-mail communication networks and comparing it to client organization's satisfaction. In collaboration with a large service provider we studied the impact of network properties on the satisfaction of client organizations. In particular, we found that social network metrics correlate with client satisfaction as measured by Net Promoter Score (NPS). A Communication Score Card is suggested as a dashboard to continuously measure client satisfaction, illustrating that data-driven analysis might help improving service providers' service quality management.

Comparing Online Community Structure of Patients of Chronic Diseases

Abstract: In this paper we compare the social network structure of people talking about Crohn's disease, Cystic Fibrosis, and Type 1 diabetes on Facebook and Twitter. We find that the Crohn's community's contributors are most emotional on Facebook and Twitter and most negative on Twitter, while the T1D community's communication network structure is most cohesive.

Heart Beats Brain: Measuring Moral Beliefs Through E-mail Analysis

Abstract: Moral beliefs are at the heart of governing a person’s behavior. In this paper, we introduce a way to automatically measure a person’s moral values through hidden “honest” signals in the person’s e-mail communication. We measured the e-mail behavior of 26 users through their e-mail interaction, calculating their seven “honest signals of collaboration” (strong leadership, balanced contribution, rotating leadership, responsiveness, honest sentiment, shared context and social capital). These honest signals—in other words, how they answered their e-mails—explained 70% of their moral values measured with the moral foundations survey. In particular, the more positive and less emotional they were in their language, the more they cared about others. We verified the results with a larger e-mail dataset of 655 employees of a services firm, where structural and temporal honest signals explained 67% of emotionality.

"Emotions are the Great Captains of our Lives": Measuring Moods through the Power of Physiological and Environmental Sensing

Abstract: The paper proposes the use of a smartwatch-based system for measuring the emotions of individuals in a classroom setting with respect to five mood variables: Activation, Tiredness, Pleasance, Quality of Presentation and Understanding. Internal (body) and external (environment) data such as movement, heart rate, noise, temperature and humidity were collected through the built-in sensors of the smartwatch. The system was verified by means of a longitudinal study that has been carried out in a series of workshops and lectures. Through experience-based sampling, participants were polled at periodic time intervals asking them to enter a self-assessment of the aforementioned mood states directly on the smartwatch. The goal was to demonstrate whether sensor data can be used to effectively predict the five moods. By resorting to a machine learning approach our system was able to predict the moods with an accuracy ranging between 89-95% for single-output classification, 92-99% for the chain classification task and of approximately 93% for the multi-output analysis. Our results showed also that body signals are better predictors compared to the external environmental variables. These results demonstrate and verify the potential of smartwatches in collecting and predicting human emotions, enabling dynamic feedback loops to enhance user experience.

Using Multivariate Statistics

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Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.