US Election Prediction: A Linguistic Analysis of US Twitter Users
This chapter outlines a process to use linguistic data collected from Twitter to predict for whom a person will vote. The linguistic analysis makes use of previous research into profiling based on frequencies of words in natural language. We use data coll
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Matthäus P. Zylka Hauke Fuehres Andrea Fronzetti Colladon Peter A. Gloor Editors
Designing Networks for Innovation and Improvisation Proceedings of the 6th International COINs Conference
Springer Proceedings in Complexity
More information about this series at http://www.springer.com/series/11637
Matthäus P. Zylka • Hauke Fuehres Andrea Fronzetti Colladon • Peter A. Gloor Editors
Designing Networks for Innovation and Improvisation Proceedings of the 6th International COINs Conference
Editors Matthäus P. Zylka Department of Information Systems & Social Networks University of Bamberg Bamberg, Germany Andrea Fronzetti Colladon Department of Enterprise Engineering Tor Vergata University of Rome Rome, Italy
Hauke Fuehres Department of Information Systems & Social Networks University of Bamberg Bamberg, Germany Peter A. Gloor MIT Center for Collective Intelligence Cambridge, MA, USA
ISSN 2213-8684 ISSN 2213-8692 (electronic) Springer Proceedings in Complexity ISBN 978-3-319-42696-9 ISBN 978-3-319-42697-6 (eBook) DOI 10.1007/978-3-319-42697-6 Library of Congress Control Number: 2016954316 © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This book contains 17 peer-reviewed contributions presented at the Sixth international COINs Conference, held in Rome, Italy, from 8 to 11 June 2016. The papers in this book cover a broad range of topics, starting with an analysis of different communities and societies through social network analysis (SNA). Classic SNA looks at the structure of networks; the papers in this book add analysis of the dynamics of network change over time, and an analysis of the content of the networks, for example, in e-mails, Tweets, or Wikipedia. Dynamic and contentbased SNA affords
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