Key Player Identification in Terrorism-Related Social Media Networks Using Centrality Measures

Gialampoukidis, Ilias; Kalpakis, George; Tsikrika, Theodora; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.1109/eisic.2016.029

Cited by 18 publications

(20 citation statements)

References 19 publications

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“…Recent works have examined the use of social media platforms by terrorist groups and organizations (Chatfield et al 2015;Klausen 2015). Moreover, key player and key community identification in terrorism-related Twitter networks has been addressed through the use of different centrality measures and community detection algorithms (Gialampoukidis et al 2016(Gialampoukidis et al , 2017. Complementary to the aforementioned research efforts, our paper analyzes several textual, spatial, temporal and social network features which, when combined, are capable of characterizing the terrorism-related nature of Twitter accounts.…”

Section: Related Workmentioning

confidence: 99%

Community-Oriented Policing and Technological Innovations

Leventakis¹,

Haberfeld²

2018

SpringerBriefs in Criminology

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SpringerBriefs in Criminology present concise summaries of cutting edge research across the fields of Criminology and Criminal Justice. It publishes small but impactful volumes of between 50-125 pages, with a clearly defined focus. The series covers a broad range of Criminology research from experimental design and methods, to brief reports and regional studies, to policy-related applications.The scope of the series spans the whole field of Criminology and Criminal Justice, with an aim to be on the leading edge and continue to advance research. The series will be international and cross-disciplinary, including a broad array of topics, including juvenile delinquency, policing, crime prevention, terrorism research, crime and place, quantitative methods, experimental research in criminology, research design and analysis, forensic science, crime prevention, victimology, criminal justice systems, psychology of law, and explanations for criminal behavior.SpringerBriefs in Criminology will be of interest to a broad range of researchers and practitioners working in Criminology and Criminal Justice Research and in related academic fields such as Sociology, Psychology, Public Health, Economics and Political Science. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 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. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland We also thank the following people f...

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Section: Related Workmentioning

confidence: 99%

Community-Oriented Policing and Technological Innovations

Leventakis¹,

Haberfeld²

2018

SpringerBriefs in Criminology

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“…In the resulting network of users, each user is represented by a node and a link between two users (i, k) exists if user n i mentions or is mentioned by user n k . We use entropy-based centralities to, first, identify key-players [10] and we then extend the method by associating key-players with their community.…”

Section: Key Terrorism Community Detection Frameworkmentioning

confidence: 99%

“…whereas Mapping Entropy Betweenness centrality [10] is defined as a function of betweenness centrality:…”

Section: Centrality-based Key Player Identificationmentioning

confidence: 99%

“…Key player identification in complex networks, on the other hand, has been mainly addressed through the use of different centrality measures; e.g. recent work [10] has used several centrality measures to rank terrorism-related Twitter accounts based on their location in the network and the topology of the network of user-to-user mentions.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, we extend the approach of [10] and propose a hybrid framework which first retrieves the key network players and then enriches the retrieved results by adding the members of a user's detected community based on the combination of centrality scores with community detection algorithms. These centrality measures, which aim to identify key-players in the terrorism domain, are estimated on social media networks based on user mentions and are compared with other popularity measures (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Terrorism-related Twitter Communities using Centrality Scores

Gialampoukidis

Kalpakis

Tsikrika

et al. 2017

Proceedings of the 2nd International Workshop on Multimedia Forensics and Security

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Social media are widely used among terrorists to communicate and disseminate their activities. User-to-user interaction (e.g. mentions, follows) leads to the formation of complex networks, with topology that reveals key-players and key-communities in the terrorism domain. Both the administrators of social media platforms and Law Enforcement Agencies seek to identify not only single users but groups of terrorism-related users so that they can reduce the impact of their information exchange efforts. To this end, we propose a novel framework that combines community detection with keyplayer identification to retrieve communities of terrorism-related social media users. Experiments show that most of the members of each retrieved key-community are already suspended by Twitter, violating its terms, and are hence associated with terrorism-oriented content with high probability.

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Identifying Terrorism-Related Key Actors in Multidimensional Social Networks

Kalpakis

Tsikrika

Vrochidis

et al. 2018

MultiMedia Modeling

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Identifying terrorism-related key actors in social media is of vital significance for law enforcement agencies and social media organizations in their effort to counter terrorism-related online activities. This work proposes a novel framework for the identification of key actors in multidimensional social networks formed by considering several different types of user relationships/interactions in social media. The framework is based on a mechanism which maps the multidimensional network to a single-layer network, where several centrality measures can then be employed for detecting the key actors. The effectiveness of the proposed framework for each centrality measure is evaluated by using well-established precision-oriented evaluation metrics against a ground truth dataset, and the experimental results indicate the promising performance of our key actor identification framework.

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Key Player Identification in Terrorism-Related Social Media Networks Using Centrality Measures

Cited by 18 publications

References 19 publications

Community-Oriented Policing and Technological Innovations

Community-Oriented Policing and Technological Innovations

Detection of Terrorism-related Twitter Communities using Centrality Scores

Identifying Terrorism-Related Key Actors in Multidimensional Social Networks

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