A multi-language approach towards the identification of suspicious users on social networks

Tundis, Andrea; Mühlhäuser, Max

doi:10.1109/ccst.2017.8167794

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…In [13], a system for supporting the surveillance and monitoring of cyber-trafficking, whose communication is centred on social media, is proposed. Furthermore, some case studies on social networks, where the criminals exploit Twitter as means to communicate among each other in order to organize their criminal activities, are presented in [22,[53][54][55].…”

Section: Crime Detection Approaches On Social Networkmentioning

confidence: 99%

Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

Tundis

Böck

Stanilescu

et al. 2020

JCSANDM

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Online social networks (OSNs) represent powerful digital tools to communicate and quickly disseminate information in a non-official way. As they are freely accessible and easy to use, criminals abuse of them for achieving their purposes, for example, by spreading propaganda and radicalising people. Unfortunately, due to their vast usage, it is not always trivial to identify criminals using them unlawfully. Machine learning techniques have shown benefits in problem solving belonging to different application domains, when, due to the huge dimension in terms of data and variables to consider, it is not feasible their manual assessment. However, since the OSNs domain is relatively young, a variety of issues related to data availability makes it difficult to apply and immediately benefit from such techniques, in supporting the detection of criminals on OSNs. In this perspective, this paper wants to share the experience conducted in using a public dataset containing information related to criminals in order to both (i) extract specific features and to build a model for the detection of terrorists on Facebook social network, and (ii) to highlight the current limits. The research methodology as well as the gathered results are fully presented and then the data-related issues, emerged from this experience, are discussed. .

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Section: Crime Detection Approaches On Social Networkmentioning

confidence: 99%

Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

Tundis

Böck

Stanilescu

et al. 2020

JCSANDM

Self Cite

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“…Semiotic is the science that studies the life of signs within the society. This is important because in addition to analyzing text, identify topics, other complementary information need be to considered such as cultural aspects, location, religion, language and so on [38]. By exploiting such approach, the current research directions are devoted to (see Fig 4):…”

Section: Cyber Tools and Servicesmentioning

confidence: 99%

Challenges and Available Solutions Against Organized Cyber-Crime and Terrorist Networks

Tundis

Huber²,

Jäger³

et al. 2017

WIT Transactions on the Built Environment

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Organized Crime (OC) and Terrorist Networks (TN) have risen to major and persistent threats for the European Union and its population. The IT growth of the past decade caused a migration of OC/TN to the cyber domain as well as the introduction of cybercrime. As a consequence, the technological dimensions of criminal activities are becoming more relevant and challenges ranging from the identification of criminal activities up to the understanding of engagement processes, are even more complicated. In this context, this paper aims to provide a discussion on OC and TN by pointing out organizational models, similarities, distinguishing features and differences in terms of their objectives. Furthermore, the main issues and available categories of solutions, in terms of models, methods and software tools are described. Finally, the importance of innovative digital and non-digital solutions is discussed as well as the current research directions are highlighted.

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“…Information in the user profile can be explicitly provided by the user (explicit user profile), or more frequently, analyzed implicitly by using interaction data between the users and the system (implicit user profile) (Gauch et al 2007). Beyond personalized or adaptive systems, user profiling can also be at the base of behavioral analysis systems for improving decision-making, such as anomaly detection systems (Kwon et al 2021;Wang et al 2018), fraud detection systems (Lausen et al 2020;Zhao et al 2016), customer scoring systems (Esmeli et al 2020;Ramkumar et al 2010), influencer or leader detection systems (Girgin 2021;Primo et al 2021), and terrorist networks (Tundis and Mühlhäuser 2017;Yadav et al 2019).…”

Section: Introductionmentioning

confidence: 99%

User Modeling and Profiling in Information Systems

Tchuente

2022

Journal of Global Information Management

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User modeling or user profiling is fundamental to manage information overload issues in many adaptive and personalized systems (e.g., recommender systems, personalized search engines, adaptive user interfaces). Although there are some literature review papers that provide an overview of existing studies in user modeling and their usage, there is currently a lack of bibliometric studies that can provide a systematic and quantitative overview of this research area. Therefore, this paper aims to complete the existing literature in this research area through a bibliometric study based on 52,027 relevant publications extracted from Scopus, a world-leading publisher-independent global citation database. The analyses enabled us to identify the most relevant publications, sources of publications, authors, institutions, countries, and their collaboration. We also identify and classify the twelve most important associated topics, along with their subtopics and their trends. Some identified weak signals in topic trend analysis also provide good ideas of potential future research directions.

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A multi-language approach towards the identification of suspicious users on social networks

Cited by 13 publications

References 12 publications

Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

Challenges and Available Solutions Against Organized Cyber-Crime and Terrorist Networks

User Modeling and Profiling in Information Systems

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