CAPER: Crawling and analysing Facebook for intelligence purposes (Contributo in atti di convegno)

Type
Label
  • CAPER: Crawling and analysing Facebook for intelligence purposes (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/ASONAM.2014.6921656 (literal)
Alternative label
  • Aliprandi C.; De Luca A.E.; Di Pietro G.; Raffaelli M.; Gazze D.; La Polla M.N.; Marchetti A.; Tesconi M. (2014)
    CAPER: Crawling and analysing Facebook for intelligence purposes
    in FOSINT - International Symposium on Foundations of Open Source Intelligence and Security Informatics, Pechino
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Aliprandi C.; De Luca A.E.; Di Pietro G.; Raffaelli M.; Gazze D.; La Polla M.N.; Marchetti A.; Tesconi M. (literal)
Pagina inizio
  • 665 (literal)
Pagina fine
  • 669 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84911164332&partnerID=q2rCbXpz (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Synthema Srl, Pisa, Italy; Istituto di Informatica e Telematica - CNR, Pisa, Italy (literal)
Titolo
  • CAPER: Crawling and analysing Facebook for intelligence purposes (literal)
Abstract
  • Organised crime uses information technology systems to communicate, work or expand its influence. The EU FP7 Security Research Project CAPER (Collaborative information, Acquisition, Processing, Exploitation and Reporting for the prevention of organised crime), created in cooperation with European Law Enforcement Agencies (LEAs), aims to build a common collaborative and information sharing platform for the detection and prevention of organised crime, which exploits Open Source Intelligence (OSINT). LEAs are becoming more inclined to using OSINT tools, and particularly tools able to manage Online Social Networks (OSNs) data. This paper presents the CAPER Facebook crawling and analysis subsystem. Heuristic algorithms have been implemented in order to extract specific properties of Facebook's social graph, in particular user interactions. To support analysis tasks specifically, extensive effort has been spent on the analysis of textual user generated content and on the recognition of named-entities, in particular person names, locations and organisations. Relationships between users and entities mentioned in posts and in related comments are created and merged into the users networks extracted from the social graph. All entity relationships are finally visualised in user-friendly network graphs. (literal)
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