Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues
Online social networks (OSNs) represent powerful digital tools to communicate and quickly disseminate information in a unofficial 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 ().
H. Abadinsky. Organized crime, 10th Ed. Wadsworth, Belmont, USA,
R. Adderley, A. Badii, and C. Wu. The automatic identification and prioritization
of criminal networks from police crime data, EuroISI 2008,
LNCS 5376, Springer-Verlag Berlin Heidelberg, pages 5–14, 2008.
R. Adderley and P. B. Mushgrove. Data mining case study: Modeling the
behavior of offenders who commit sexual assaults, ACM SIGKDD 2001
International Conference on Knowledge Discovery and Data Mining,
New York, pages 215–220, 2001.
M. Chau, J. Xu, and H. Chen. Extracting meaningful entities from
police narrative reports. In: National Conference on Digital Government
H. G. Goldberg and R.W. H.Wong. Restructuring transactional data for
link analysis. In: FinCEN AI System, AAAI Fall Symposium, 1998.
G. C. Oatley, J. Zeleznikov, and B. W. Ewart. Matching and predicting
crimes. At: AI2004-The 24th SGAI International Conference on
Knowledge Based Systems and Applications of Artificial Intelligence,
D. B. Skillicorn. Clusters within clusters: SVD and counterterrorism,
At: Workshop on Data Mining for Counterterrorism and Security, 2003.
A. Tundis, G. Bhatia, A. Jain, and M. Mühlhäuser. “Supporting the
Identification and the Assessment of Suspicious Users on Twitter Social
Media,” 2018 IEEE 17th International Symposium on Network Computing
and Applications (NCA), Cambridge, MA, 2018, pp. 1–10.
J. Xu and H. Chen: CrimeNet Explorer: A Framework for Criminal
Network Knowledge Discovery. In: ACM Transactions on Information
Systems, Vol. 23, No. 2, pages 201–226, 2005.
J. Xu and H. Chen: Fighting Organised Crimes: using shortest-path
algorithms to identify associations in criminal networks. In: Decision
Support Systems, vol. 38, no. 3, pages 473–487, 2003.
J. Xu and H. Chen: The topology of dark networks. In: Communications
of the ACM, Vol. 51, No. 10, pages 58–65, 2008.
B. Wellman: The network community: An introduction. In B. Wellman
(Ed.). Networks in the Global Community, pages 1–47,Westview Press,
Boulder, USA, 1999.
W. Chung, E. Mustaine, and D. Zeng. “Criminal intelligence surveillance
and monitoring on social media: Cases of cyber-trafficking,” IEEE
International Conference on Intelligence and Security Informatics (ISI),
Beijing, 2017, pp. 191–193. doi: 10.1109/ISI.2017.8004908.
T. De Smedt, G. De Pauw and P. Van Ostaeyen, 2018. Automatic
Detection of Online Jihadist Hate Speech. In Computational Linguistics
M. M. Janeela Theresa and V. Joseph Raj, 2011. “Analogy making in
criminal law with neural network,” International Conference on Emerging
Trends in Electrical and Computer Technology, Nagercoil, 2011,
pp. 772–775. doi: 10.1109/ICETECT.2011.5760222.
L. Kaati, E. Omer, N. Prucha, and A. Shrestha, 2015. “Detecting Multipliers
of Jihadism on Twitter,” IEEE International Conference on Data
MiningWorkshop (ICDMW), Atlantic City, NJ, 2015, pp. 954–960. doi:
Q. A. Memon and S. Mehboob, 2003. “Crime investigation and analysis
using neural nets,” 7th International Multi Topic Conference, Islamabad,
pp. 346–350. doi: 10.1109/INMIC.2003.1416748.
M. Nakib, R. T. Khan, M. S. Hasan, and J. Uddin, 2018. “Crime Scene
Prediction by Detecting Threatening Objects Using Convolutional Neural
Network,” Int. Conference on Computer, Communication, Chemical,
Material and Electronic Engineering (IC4ME2), Rajshahi, 2018,
pp. 1–4. doi: 10.1109/IC4ME2.2018.8465583.
D. Parekh, A. Amarasingam, L. Dawson, and D. Ruths, 2018. Studying
Jihadists on Social Media: A Critique of Data Collection Methodologies.
In: Perspectives on Terrorism, vol. 12(3).
R. R. Petersen. 2013. Criminal network investigation is all about hypertext.
SIGWEB Newsl. Autumn, Article 2 (September 2013), 7 pages.
M. A. Rashidan, Y. M. Mustafah, S. B. A. Hamid, N. A. Zainuddin, and
N. N. A. Aziz, 2014. “Detection of Different Classes Moving Object in
Public Surveillance Using Artificial Neural Network (ANN),” International
Conference on Computer and Communication Engineering, Kuala
Lumpur, 2014, pp. 240–242. doi: 10.1109/ICCCE.2014.75.
S. Sava¸s and N. Topaloˇglu, 2017. “Crime intelligence from social
media: A case study,” IEEE 14th International Scientific Conference
on Informatics, Poprad, 2017, pp. 313–317. doi: 10.1109/INFORMATICS.
JJATT 2018 – John Jay & ARTIS Transnational Terrorism Database –
online availabe at http://doitapps.jjay.cuny.edu/jjatt/attributes.php.
F. Ozgul and Z. Erdem. 2012. Detecting Criminal Networks Using
Social Similarity. In Proceedings of the 2012 International Conference
on Advances in Social Networks Analysis and Mining (ASONAM
(ASONAM’12). IEEE Computer Society,Washington, DC, USA,
–585. doi: 10.1109/ASONAM.2012.98.
F. Ozgul, Z. Erdem, C. Bowerman, and J. Bondy (2010). Combined
Detection Model for Criminal Network Detection. In: Chen H., Chau
M., Li S., Urs S., Srinivasa S.,Wang G.A. (eds) Intelligence and Security
Informatics. PAISI 2010. Lecture Notes in Computer Science, vol 6122.
Springer, Berlin, Heidelberg.
F. Ozgul, M. Gok, Z. Erdem, and Y. Ozal (2012). Detecting criminal
networks: SNA models are compared to proprietary models. 156–158.
A. Berzinji, F. S. Abdullah, and A. H. Kakei, “Analysis of Terrorist
Groups on Facebook,” 2013 European Intelligence and Security
Informatics Conference, Uppsala, 2013.
A. T. Chatfield, C. G. Reddick, and U. Brajawidagda, “Tweeting propaganda,
radicalization and recruitment: Islamic state supporters multisided
twitter networks,” in Proceedings of the 16th Annual International
Conference on Digital Government Research. ACM, 2015.
R. A. Bates, “Dancing with wolves: Today’s lone wolf terrorists,” The
Journal of Public and Professional Sociology, vol. 4, no. 1: p. 1, 2012.
D. Masciandaro, Global financial crime: terrorism, money laundering
and offshore centres. Taylor & Francis, 2017.
G. Cheng and X. Tong, “Fuzzy Clustering Multiple Kernel Support
Vector Machine,” 2018 International Conference on Wavelet Analysis
and Pattern Recognition (ICWAPR), Chengdu, 2018, pp. 7–12.
P. Perner, “How to Compare and Interpret Two Learnt Decision
Trees from the Same Domain?” 2013 27th International Conference
on Advanced Information Networking and Applications Workshops,
Barcelona, 2013, pp. 318–322.
L. Q. Yu and F. S. Rong, “Stock Market Forecasting Research Based on
Neural Network and Pattern Matching,” 2010 International Conference
on E-Business and E-Government, Guangzhou, 2010, pp. 1940–1943.
Valencia Local Police – Online http://www.carismand.eu/valencia-city
-council-local-police-plv-spain.html - http://forensor-project.eu/author
R. Borum. Radicalization into Violent Extremism I: A Review of Social
Science Theories. Journal of Strategic Security. Vol. 4 Issue 4. (2011)
A. P. Schmid (2013-03-27). “Radicalisation, De-Radicalisation,
Counter-Radicalisation: A Conceptual Discussion and Literature
Review”. The International Centre for Counter-Terrorism – The Hague
TAKEDOWN – A EU H2020 Research project – Online available at
Vincenzo Ruggiero, “Organized Crime and Terrorist Networks”,
London, Routledge, 1st Edition, 2019, pp. 220, doi: 10.4324/
S. Kavitha, S. Varuna, and R. Ramya. “A comparative analysis on
linear regression and support vector regression”. In Proceedings of
the 2016 Online International Conference on Green Engineering and
Technologies (IC-GET), Kuala Lumpur, Malaysia, 25–27 July 2016;
L. Burita, “Information Analysis on Facebook,” 2019 Communication
and Information Technologies (KIT), Vysoke Tatry, Slovakia, 2019,
pp. 1–5. doi: 10.23919/KIT.2019.8883471.
J. Clement, Statista – November 2019. https://www.statista.com/statist
A. Moltzau, Towards Data Science – July 2019. https://towardsdatasci
J. Hu, M. Liu, and J. Zhang, “A semantic model for academic social network
analysis,” 2014 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining (ASONAM 2014), Beijing,
, pp. 310–313. doi: 10.1109/ASONAM.2014.6921602.
N. Akhtar, H. Javed, and G. Sengar, “Analysis of Facebook Social
Network,” 2013 5th International Conference and Computational Intelligence
and Communication Networks, Mathura, 2013, pp. 451–454. doi:
A. Berzinji, F. S. Abdullah, and A. H. Kakei, “Analysis of
Terrorist Groups on Facebook,” 2013 European Intelligence and
Security Informatics Conference, Uppsala, 2013, pp. 221–221.
C. Aliprandi, et al. “CAPER: Crawling and analysing Facebook for
intelligence purposes,” 2014 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining (ASONAM 2014),
Beijing, 2014, pp. 665–669. doi: 10.1109/ASONAM.2014.6921656.
M. Ketcham, T. Ganokratanaa, and S. Bansin, “The Forensic
Algorithm on Facebook Using Natural Language Processing,”
12th International Conference on Signal-Image Technology &
Internet-Based Systems (SITIS), Naples, 2016, pp. 624–627. doi:
CHAMPIONs – A European Union’s Internal Security Fund – Police
research project – Online available at https://www.championsproject.
ARMOUR – A European Union’s Internal Security Fund – Police
research project – Online available at https://www.armourproject.eu/.
CARPER – A Seventh Framework Programme for Research and Technological
Development – Online available at http://www.fp7-caper.
S.Wagstyl, G. Chazan, and T. Buck, ‘MerkelWins Fourth Term but Far-
Right Populists Make Gains’, Financial Times. Sep 25, 2017. Online
G. Delanty (2017). A divided nation in a divided Europe: Emerging
cleavages and the crisis of European integration. Brexit: Sociological
A. Tundis, G. Bhatia, A. Jain, and M. Mühlhäuser, “Supporting the
Identification and the Assessment of Suspicious Users on Twitter Social
Media,” Proceeding of the IEEE 17th International Symposium on
Network Computing and Applications (NCA), Cambridge, MA, 2018,
pp. 1–10. doi: 10.1109/NCA.2018.8548321.
A. Tundis, A. Jain, G. Bhatia, and M. Muhlhauser, “Similarity Analysis
of Criminals on Social Networks: An Example on Twitter,”
Proceeding of the 28th International Conference on Computer Communication
and Networks (ICCCN), Valencia, Spain, 2019, pp. 1–9.
A. Tundis and M. Mühlhäuser, “A multi-language approach towards the
identification of suspicious users on social networks,” Proceeding of the
International Carnahan Conference on Security Technology (ICCST),
Madrid, 2017, pp. 1–6. doi: 10.1109/CCST.2017.8167794.
A. Tundis and M. Mühlhäuser, “The role of Information and Communication
Technology (ICT) in modern criminal organizations.” Book
Chapter in: Organized Crime and Terrorist Networks, London, Routledge,
V. Jirovský, A. Pastorek, A. Tundis, and Max Mühlhäuser. “Cybercrime
and Organized Crime,” Proceeding of the International Conference on
Availability, Reliability and Security (ARES 2018), Hamburg, Germany,
August 27–30, 2018, doi: 10.1145/3230833.3233288.
A. Tundis, W. Mazurczyk, and M. Mühlhäuser. “A review of network
vulnerabilities scanning tools: types, capabilities and functioning.”
Proceeding of the International Conference on Availability, Reliability
and Security (ARES 2018), Hamburg, Germany, August 27–30, 2018,
A. Tundis, L. Böck, V. Stanilescu and M. Mühlhäuser. “Limits in
the data for detecting criminals on social media,” Proceeding of
the International Conference on Availability, Reliability and Security
(ARES 2019), Kent, Canterbury, UK, August 26–29, 2019. doi:
Copyright (c) 2020 Journal of Cyber Security and Mobility
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.