Social networks data analysis with semantics: application to the radicalization problem

Barhamgi, Mahmoud; Masmoudi, Abir; Lara-Cabrera, Raúl; Camacho, David

doi:10.1007/s12652-018-0968-z

Cited by 9 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We have also reviewed other vocabularies, such as the radicalization ontology [49]. This ontology includes a central class Message that represents social media messages.…”

Section: Semantic Modelingmentioning

confidence: 99%

Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

View full text Add to dashboard Cite

Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surveys. This article proposes to complement social science approaches with computational methods to detect these factors automatically by analyzing the language signals expressed in social networks. To this end, the article categorizes radicalization drivers and factors following the micro, meso, and macro levels used in the social sciences. It identifies the corresponding language signals and available language resources. Then, a computational system is developed to monitor these language signals. In addition, this article proposes semantic technologies since they offer unique exploration, query, and discovery capabilities. The system was evaluated based on a set of competency questions that show the benefits of this approach.

show abstract

“…We have also reviewed other vocabularies, such as the radicalization ontology [49]. This ontology includes a central class Message that represents social media messages.…”

Section: Semantic Modelingmentioning

confidence: 99%

Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Barhamgi et al [69] explore the use of the semantic web and domain ontologies to automatically detect indicators and warning signals, emitted from messages and posts in social networks, during the radicalization process of vulnerable individuals and their recruitment from terrorist organizations. Specifically they devise an ontology for the radicalization domain and exploit it to automatically annotate social messages (tweets).…”

Section: Document Classificationmentioning

confidence: 99%

Ontology-Based Feature Selection: A Survey

2021

View full text Add to dashboard Cite

The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

show abstract

“…No levantamento da literatura de trabalhos relacionados, percebemos que são utilizadas algumas técnicas de Machine Learning [Wang 2006]. O uso de ontologias semânticas na Web em domínio para extrair automaticamente indicadores de radicalização de mensagens e postagens em redes sociais, utilizadas em domínio da radicalização em mensagens, foram encontrados e pesquisados [Barhamgi 2018]. Há estudos que englobam a detecção automática de recrutamento de pessoas a grupos extremistas como o estado islâmico em redes sociais que determinem se um usuário pertence a estes grupos ou se está em processo de recrutamento, a partir das informações relacionadas a nomes de usuários, seu tipo de perfil, redes de amigos e conteúdo textual desses usuários [Alvari 2019].…”

Section: Trabalhos Relacionadosunclassified

Sistema de Informações de Milícias: Proposta de um Arcabouço Analítico para Análise de Textos em Dados de Denúncia

Brito¹,

Motta²,

Filho³

et al. 2020

Anais Do Seminário Integrado De Software E Hardware (SEMISH 2020)

View full text Add to dashboard Cite

Este artigo apresenta um arcabouço analítico para análise de textos em denúncias na base de dados do Disque Denúncia, fornecendo um sistema de informação denominado SIM Sistema de Informações de Milícias. Por meio deste, buscamos informações e fizemos descobertas sobre personas e entidades envolvidas com as Milícias no Estado do Rio de Janeiro por meio do estudo de caso aplicado no período de 2012 a 2019 em denúncias.

show abstract

Social networks data analysis with semantics: application to the radicalization problem

Cited by 9 publications

References 9 publications

Making Sense of Language Signals for Monitoring Radicalization

Making Sense of Language Signals for Monitoring Radicalization

Ontology-Based Feature Selection: A Survey

Sistema de Informações de Milícias: Proposta de um Arcabouço Analítico para Análise de Textos em Dados de Denúncia

Contact Info

Product

Resources

About