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Cyber Security Researchers on Online Social Networks: From the Lens of the UK’s ACEs-CSR on Twitter

Mahaini, Mohamad Imad, Li, Shujun (2023) Cyber Security Researchers on Online Social Networks: From the Lens of the UK’s ACEs-CSR on Twitter. In: Lecture Notes in Computer Science. Security and Privacy in Social Networks and Big Data: 9th International Symposium, SocialSec 2023, Canterbury, UK, August 14–16, 2023, Proceedings. Lecture Notes in Computer Science , 14097. pp. 129-148. Springer, Singapore E-ISBN 978-981-99-5177-2. (doi:10.1007/978-981-99-5177-2_8) (KAR id:102392)

Abstract

Much work in the literature has studied different types of cyber security related users and communities on OSNs, such as activists, hacktivists, hackers, cyber criminals. A few studies also covered no-expert users who discussed cyber security related topics, however, to the best of our knowledge, none has studied activities of cyber security researchers on OSNs. This paper fills this gap using a data-driven analysis of the presence of the UK’s Academic Centres of Excellence in Cyber Security Research (ACEs-CSR) on Twitter. We created machine learning classifiers to identify cyber security and research related accounts. Then, starting from 19 seed accounts of the ACEs-CSR, a social network graph of 1,817 research-related accounts that were followers or friends of at least one ACE-CSR was constructed. We conducted a comprehensive analysis of the data we collected: a social structural analysis of the social graph; a topic modelling analysis to identify the main topics discussed publicly by researchers in ACEs-CSR network, and a sentiment analysis of how researchers perceived the ACE-CSR programme and the ACEs-CSR. Our study revealed several findings: 1) graph-based analysis and community detection algorithms are useful in detecting sub-communities of researchers to help understand how they are formed and what they represent; 2) topic modelling can identify topics discussed by cyber security researchers (e.g., cyber security incidents, vulnerabilities, threats, privacy, data protection laws, cryptography, research, education, cyber conflict, and politics); and 3) sentiment analysis showed a generally positive sentiment about the ACE-CSR programme and ACEs-CSR. Our work showed the feasibility and usefulness of large-scale automated analyses of cyber security researchers on Twitter.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-981-99-5177-2_8
Uncontrolled keywords: Cyber Security, Machine Learning, Online Social Network, Community Detection, Natural Language Processing, Topic Modelling, Sentiment Analysis, Twitter
Subjects: H Social Sciences > HT Communities. Classes. Races
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76 Computer software > QA76.76.I59 Interactive media, hypermedia
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5105 Data transmission systems > TK5105.5 Computer networks > TK5105.875.I57 Internet
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5105.888 World Wide Web
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
University-wide institutes > Institute of Cyber Security for Society
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Shujun Li
Date Deposited: 09 Aug 2023 17:22 UTC
Last Modified: 05 Nov 2024 13:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/102392 (The current URI for this page, for reference purposes)

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