Extracting criminals’ information and discovering their network are techniques that investigators often rely on to get extra information about criminal incidents and potential criminals. With the recent advances of the Web, a.k.a. Web 2.0, it has become a rich source of data which provides a variety of information sources. In this article, we propose an integrated framework that combines a variety of available components and makes use of different sources of information provided on the Web to get a better knowledge about criminals or terrorists (we will use criminals to cover all terrorists in the rest of this article). Our system extracts criminals’ information and their corresponding network using Web sources, such as online newspapers, official reports, and social media. It uses text analysis to identify key persons and topics from crawled Web documents. We build a criminal graph from the analysed text based on the co-occurrence of mentioning of criminals. Further analysis is applied on the constructed graph to get key people, hidden relationships and interactions between criminals, as well as hierarchical criminal groups within a network. For every process in the framework, we analysed various available works and implementations that could be used in the process. While analysing social media posts, we identified several challenges which show what solutions could be used for that purpose. Finally, we provide a Web application which implements the proposed framework. It also shows how helpful and efficient the system is in extracting and analysing criminal information.
NathSV. Crime pattern detection using data mining. In: Proceedings of the web intelligence and intelligent agent technology workshops, Hong Kong, China, 18–22 December 2006, pp. 41–44. New York: IEEE.
2.
CragliaMHainingRWilesP. A comparative evaluation of approaches to urban crime pattern analysis. Urban Studies2000; 37(4): 711–729.
3.
BogahawatteKAdikariS.Intelligent criminal identification system. In: Proceedings of the 8th international conference on computer science & education (ICCSE), Colombo, Sri Lanka, 26–28 April 2013, pp. 633–638. New York: IEEE.
4.
ChenHChungWXuJJet al. Crime data mining: a general framework and some examples. Computer2004; 37(4): 50–56.
5.
AnwarTAbulaishM. A social graph based text mining framework for chat log investigation. Digital Investigation2014; 11(4): 349–362.
6.
KuCHIriberriALeroyG.Crime information extraction from police and witness narrative reports. In: Proceedings of the Technologies for Homeland Security, Waltham, May 12–13 May 2008, pp. 193–198. New York: IEEE.
7.
HosseinkhaniJChaprutSTaherdoostH.Criminal network mining by web structure and content mining. In: Proceedings of the international conference on information security and privacy, advances in remote sensing, finite differences and information security, Prague, 24–26 September 2012.
8.
YangCCNgTD.Terrorism and crime related weblog social network: link, content analysis and information visualization. In: Proceedings of the intelligence and security informatics, New Brunswick, NJ, 23–24 May 2007, pp. 55–58. New York: IEEE.
9.
ZhouDManavogluELiJet al. Probabilistic models for discovering e-communities. In: Proceedings of the 15th international conference on World Wide Web, Edinburg, 23–26 May 2006, pp. 173–182. New York: ACM.
10.
PramanikMILauRYChowdhuryMKH. Automatic crime detector: a framework for criminal pattern detection in big data era, 2016, https://aisel.aisnet.org/pacis2016/311/.
11.
SarvariHAbozinadahEMbaziiraAet al. Constructing and analyzing criminal networks. In: Proceedings of the security and privacy workshops (SPW), San Jose, CA, 17–18 May 2014, pp. 84–91. New York: IEEE.
12.
TahaKYooPD. Siimco: a forensic investigation tool for identifying the influential members of a criminal organization. IEEE T Inform Foren Secur2016; 11(4): 811–822.
13.
ArulanandamRSavarimuthuBTRPurvisMA.Extracting crime information from online newspaper articles. In: Proceedings of the Second Australasian web conference-volume 155, Auckland, 20–23 January 2014, pp. 31–38. New York: ACM.
14.
JayaweeraISajeewaCLiyanageSet al. Crime analytics: analysis of crimes through newspaper articles. In: Proceedings of the Moratuwa Engineering Research Conference (Mercon), Moratuwa, Sri Lanka, 7–8 April 2015, pp. 277–282. New York: IEEE.
ChoiDKoBKimHet al. Text analysis for detecting terrorism-related articles on the web. J Network Comput Appl2014; 38: 16–21.
17.
MillerGA. Wordnet: a lexical database for English. Commun ACM1995; 38: 39–41.
18.
RiloffE. Automatically constructing a dictionary for information extraction tasks. In: Proceedings of the eleventh national conference on Artificial intelligence, Washington, DC, 11–15 July 1993, pp. 811–816. Reston, VA: AAAI.
19.
RiloffE. Little words can make a big difference for text classification. In: Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval, Seattle, WA, 9–13 July 1995, pp. 130–136. New York: ACM.
20.
ConlonSJAbrahamsASSimmonsLL. Terrorism information extraction from online reports. J Comput Inform Syst2015; 55(3): 20–28.
21.
BirdS. Nltk: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive Presentation Sessions, pp. 69–72, 2006, Association for Computational Linguistics, https://www.aclweb.org/anthology/P04-3031/
HeppleM. Independence and commitment: assumptions for rapid training and execution of rule-based pos taggers. In: Proceedings of the 38th annual meeting on association for computational linguistics, Hong Kong, 3–6 October 2000, pp. 278–277. New York: ACM.
24.
ManningCDSurdeanuMBauerJet al. The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, Baltimore, MD, 22–27 June 2014, pp. 55–60. ACL Anthology.
25.
ToutanovaKKleinDManningCDet al. Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 conference of the North American chapter of the association for computational linguistics on human language technology-volume 1, Edmonton, AB, Canada, 27 May–1 June 2003, pp. 173–180. New York: ACM.
26.
DerczynskiLMaynardDAswaniNet al. Microblog-genre noise and impact on semantic annotation accuracy. In: Proceedings of the 24th ACM conference on hypertext and social media, Paris, 1–3 May 2013, pp. 21–30. New York: ACM.
27.
BontchevaKDerczynskiLFunkAet al. TwitIE: an open-source information extraction pipeline for microblog text. In: Proceedings of the international conference recent advances in natural language processing, Hissar, Bulgaria, 7–13 September 2013, pp. 83–90. ACL Anthology.
28.
GimpelKSchneiderNO’ConnorBet al. Part-of-speech tagging for twitter: annotation, features, and experiments. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies: short papers-volume 2, Portland, OR, 19–24 June 2011, pp. 42–47. New York: ACM.
29.
FinkelJRGrenagerTManningC.Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd annual meeting on association for computational linguistics, Ann Arbor, MI, 25–30 June 2005, pp. 363–370. New York: ACM.
30.
RitterAClarkSEtzioniOet al. Named entity recognition in tweets: an experimental study. In: Proceedings of the conference on empirical methods in natural language processing, Edinburgh, 27–31 July 2011, pp. 1524–1534. New York: ACM.
31.
MihalceaRTarauP. Textrank: bringing order into text. Proc Empric Meth2004; 4: 404–411.
32.
MedelyanOWittenIH. Thesaurus based automatic keyphrase indexing. In: Proceedings of the 6th ACM/IEEE-CS joint conference on digital libraries, Chapel Hill, NC, 11–15 June 2006, pp. 296–297. New York: ACM.
33.
SaltonGMcGillMJ. Introduction to modern information retrieval. New York: ACM, 1986.
34.
MedelyanOWittenIHMilneD. Topic indexing with wikipedia. Proc AAAI2008; 1: 19–24.
SaifHHeYAlaniH. Semantic sentiment analysis of twitter. In: Proceedings of the 11th international semantic web conference, Boston, MA, 11–15 November 2012, pp. 504–528. New York: Springer.
37.
MathioudakisMKoudasN. TwitterMonitor: trend detection over the Twitter stream. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, Indianapolis, IN, 6–10 June 2010, pp. 1155–1158. New York: ACM.
38.
YangSHKolczASchlaikjerAet al. Large-scale high-precision topic modeling on Twitter. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, New York, 24–27 August 2014, pp. 1907–1916. New York: ACM.
39.
BastianMHeymannSJacomyMet al. Gephi: an open source software for exploring and manipulating networks. Int Conf Weblog Soc Med2009; 8: 361–362.
40.
CsardiGNepuszT. The igraph software package for complex network research. Int J Complex Syst1695(5): 12006–12009.
41.
KoochakzadehNSarrafAKianmehrKet al. NetDriller: a powerful social network analysis tool. In: Proceedings of the 11th international conference on data mining workshops (ICDMW), Vancouver, BC, Canada, 11 December 2011, pp. 1235–1238. New York: IEEE.
42.
Al-ZaidyRFungBCYoussefAMet al. Mining criminal networks from unstructured text documents. Digital Invest2012; 8(3): 147–160.
43.
LiuBHsuWMaY. Integrating classification and association rule mining. In: Proceedings of the fourth international conference on knowledge discovery and data mining, New York, 27–31 August 1998.
44.
NewmanME. Clustering and preferential attachment in growing networks. Phys Rev64: 025102.
45.
AdamicLAAdarE. Friends and neighbors on the web. Soc Netw2003; 25(3): 211–230.
46.
AfraSAksacxAOzyerTet al. Link prediction by network analysis. In: KawashJAgarwalNÖzyerT (eds) Prediction and inference from social networks and social media. New York: Springer, 2017, pp. 97–114.
47.
BakerSA. From the criminal crowd to the ‘mediated crowd’: the impact of social media on the 2011 English riots. Safer Commun2012; 11(1): 40–49.
48.
LancichinettiAFortunatoSRadicchiF. Benchmark graphs for testing community detection algorithms. Phys Rev2008; 78: 046110.
