Students Assessing Digital News and Misinformation
Previous research has highlighted how young people struggle to distinguish news from misinformation. In this study, we investigate how ca. 400 students determine the trustworthiness of false, biased and credible news. We find that students use different s
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Max van Duijn · Mike Preuss · Viktoria Spaiser · Frank Takes · Suzan Verberne (Eds.)
Disinformation in Open Online Media Second Multidisciplinary International Symposium, MISDOOM 2020 Leiden, The Netherlands, October 26–27, 2020 Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7409
Max van Duijn Mike Preuss Viktoria Spaiser Frank Takes Suzan Verberne (Eds.) •
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Disinformation in Open Online Media Second Multidisciplinary International Symposium, MISDOOM 2020 Leiden, The Netherlands, October 26–27, 2020 Proceedings
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Editors Max van Duijn Leiden Institute of Advanced Computer Science Leiden University Leiden, The Netherlands Viktoria Spaiser School of Politics and International Studies University of Leeds Leeds, UK Suzan Verberne Leiden Institute of Advanced Computer Science Leiden University Leiden, The Netherlands
Mike Preuss Leiden Institute of Advanced Computer Science Leiden University Leiden, The Netherlands Frank Takes Leiden Institute of Advanced Computer Science Leiden University Leiden, The Netherlands
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-61840-7 ISBN 978-3-030-61841-4 (eBook) https://doi.org/10.1007/978-3-030-61841-4 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © Springer Nature Switzerland AG 2020 Chapters “Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models” and “Do Online Trolling Strategies Differ in Political and Interest Forums: Early Results” are licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter. 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
