Peter A. Gloor

Bio: Peter A. Gloor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topic(s): Social network analysis & Social network. The author has an hindex of 37, co-authored 211 publication(s) receiving 4918 citation(s). Previous affiliations of Peter A. Gloor include University of Cologne & Union Bank of Switzerland.

Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

Abstract: This paper describes early work trying to predict stock market indicators such as Dow Jones, NASDAQ and S&P 500 by analyzing Twitter posts. We collected the twitter feeds for six months and got a randomized subsample of about one hundredth of the full volume of all tweets. We measured collective hope and fear on each day and analyzed the correlation between these indices and the stock market indicators. We found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed significant positive correlation to VIX. It therefore seems that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.

Swarm Creativity: Competitive Advantage through Collaborative Innovation Networks

Abstract: Introduction: At the Tipping Point 1. COINs and Their Benefits 2. Collaborative Innovation Through Swarm Creativity 3. The DNA of COINS 4. Ethical Codes in Small Worlds 5. Real-Life Examples: Lessons Learned from COINs 6. COINs and Communications Technology Appendix A CKN Appendix B TeCFlow Appendix C KFO

The power of prediction with social media

Abstract: – Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance. , – Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided. , – A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data. , – This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.

In the shades of the uncanny valley: An experimental study of human–chatbot interaction

Abstract: This project has been carried out in the context of recent major developments in botics and more widespread usage of virtual agents in personal and professional sphere. The general purpose of the experiment was to thoroughly examine the character of the human–non-human interaction process. Thus, in the paper, we present a study of human–chatbot interaction, focusing on the affective responses of users to different types of interfaces with which they interact. The experiment consisted of two parts: measurement of psychophysiological reactions of chatbot users and a detailed questionnaire that focused on assessing interactions and willingness to collaborate with a bot. In the first quantitative stage, participants interacted with a chatbot, either with a simple text chatbot (control group) or an avatar reading its responses in addition to only presenting them on the screen (experimental group. We gathered the following psychophysiological data from participants: electromyography (EMG), respirometer (RSP), electrocardiography (ECG), and electrodermal activity (EDA). In the last, declarative stage, participants filled out a series of questionnaires related to the experience of interacting with (chat)bots and to the overall human–(chat)bot collaboration assessment. The theory of planned behaviour survey investigated attitude towards cooperation with chatbots in the future. The social presence survey checked how much the chatbot was considered to be a “real” person. The anthropomorphism scale measured the extent to which the chatbot seems humanlike. Our particular focus was on the so-called uncanny valley effect, consisting of the feeling of eeriness and discomfort towards a given medium or technology that frequently appears in various kinds of human–machine interactions. Our results show that participants were experiencing lesser uncanny effects and less negative affect in cooperation with a simpler text chatbot than with the more complex, animated avatar chatbot. The simple chatbot have also induced less intense psychophysiological reactions. Despite major developments in botics, the user’s affective responses towards bots have frequently been neglected. In our view, understanding the user’s side may be crucial for designing better chatbots in the future and, thus, can contribute to advancing the field of human–computer interaction.

Visualization of Communication Patterns in Collaborative Innovation Networks - Analysis of Some W3C Working Groups

Abstract: Collaborative Innovation Networks (COINs) are groups of self-motivated individuals from various parts of an organization or from multiple organizations, empowered by the Internet, who work together on a new idea, driven by a common vision. In this paper we report first results of a project that examines innovation networks by analyzing the e-mail archives of some W3C (WWW consortium) working groups. These groups exhibit ideal characteristics for our purpose, as they form truly global networks working together over the Internet to develop next generation technologies. We first describe the software tools we developed to visualize the temporal communication flow, which represent communication patterns as directed acyclic graphs, We then show initial results, which revealed significant variations between the communication patterns and network structures of the different groups., We were also able to identify distinctive communication patterns among group leaders, both those who were officially appointed and other who were assuming unofficial coordinating roles.

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.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.