Multi agent system approach for vulnerability analysis of online social network profiles over time

  • Authors:
  • Ruqayya Abdulrahman;Sophia Alim;Daniel Neagu;D. R. W. Holton;Mick Ridley

  • Affiliations:
  • School of Computing, Informatics and Media, University of Bradford, Bradford, BD7 1DP, UK/ College of Computer Science and Engineering, Taibah University, Al-madinah Al-munawwara, Saudi Arabia.;School of Computing, Informatics and Media, University of Bradford, Bradford, BD7 1DP, UK.;School of Computing, Informatics and Media, University of Bradford, Bradford, BD7 1DP, UK.;School of Computing, Informatics and Media, University of Bradford, Bradford, BD7 1DP, UK.;School of Computing, Informatics and Media, University of Bradford, Bradford, BD7 1DP, UK

  • Venue:
  • International Journal of Knowledge and Web Intelligence
  • Year:
  • 2012

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Abstract

Nowadays, we witness a flood of continuously changing information from a variety of web sources. New challenges to track information changes in real time require new methods in web information retrieval using multi agent system (MAS) technology. This research continues previous work on extracting data from online social networks (OSNs) by using an agent in each user profile to monitor its updates, which are sent to a controller agent that saves a history of each user's activity in a local repository, as well as applying a vulnerability measure to users' profiles. An algorithm making use of MAS within the online social network retrieval system (OSNRS) is proposed. Our experiments on data extraction show that using MAS simplifies the process of tracking profile's history and opens the opportunity of understanding the dynamic behaviour of OSN users especially when it is combined with text mining. The application of the vulnerability measure over time highlighted that in the case of this experiment the structure of the node's network, rather than the contents of the node, changed over time. The validation of the vulnerability measure showed that friends of a profile, that disclose their personal details online, may not leak personal details about the profile.