Towards discovering criminal communities from textual data

Rabeah Al-Zaidy; Benjamin C.M. Fung; Amr M. Youssef

doi:10.1145/1982185.1982225

Towards discovering criminal communities from textual data

Rabeah Al-Zaidy^*
, Benjamin C.M. Fung
, Amr M. Youssef

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

In many criminal cases, forensically collected data contain valuable information about a suspect's social networks. An investigator often has to manually extract information from the collected text documents and enter it into a police database for further investigation with criminal network analysis tools. In this paper, we propose a method to discover criminal communities, to analyze the closeness of the members in the communities, and to extract useful information for crime investigation directly from the text documents. The proposed method, together with the implemented software tool, has received positive feedbacks from the digital forensics team of a law enforcement unit in Canada.

Original language	English
Title of host publication	26th Annual ACM Symposium on Applied Computing, SAC 2011
Pages	172-177
Number of pages	6
DOIs	https://doi.org/10.1145/1982185.1982225
State	Published - 2011
Externally published	Yes
Event	26th Annual ACM Symposium on Applied Computing, SAC 2011 - TaiChung, Taiwan, Province of China Duration: 21 Mar 2011 → 24 Mar 2011

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Conference

Conference	26th Annual ACM Symposium on Applied Computing, SAC 2011
Country/Territory	Taiwan, Province of China
City	TaiChung
Period	21/03/11 → 24/03/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

community discovery
crime investigation
data mining
forensic analysis

ASJC Scopus subject areas

Software

Access to Document

10.1145/1982185.1982225

Cite this

@inproceedings{df575eb96358486fa01d0ce95ae91194,

title = "Towards discovering criminal communities from textual data",

abstract = "In many criminal cases, forensically collected data contain valuable information about a suspect's social networks. An investigator often has to manually extract information from the collected text documents and enter it into a police database for further investigation with criminal network analysis tools. In this paper, we propose a method to discover criminal communities, to analyze the closeness of the members in the communities, and to extract useful information for crime investigation directly from the text documents. The proposed method, together with the implemented software tool, has received positive feedbacks from the digital forensics team of a law enforcement unit in Canada.",

keywords = "community discovery, crime investigation, data mining, forensic analysis",

author = "Rabeah Al-Zaidy and Fung, \{Benjamin C.M.\} and Youssef, \{Amr M.\}",

year = "2011",

doi = "10.1145/1982185.1982225",

language = "English",

isbn = "9781450301138",

series = "Proceedings of the ACM Symposium on Applied Computing",

pages = "172--177",

booktitle = "26th Annual ACM Symposium on Applied Computing, SAC 2011",

note = "26th Annual ACM Symposium on Applied Computing, SAC 2011 ; Conference date: 21-03-2011 Through 24-03-2011",

}

Al-Zaidy, R, Fung, BCM & Youssef, AM 2011, Towards discovering criminal communities from textual data. in 26th Annual ACM Symposium on Applied Computing, SAC 2011. Proceedings of the ACM Symposium on Applied Computing, pp. 172-177, 26th Annual ACM Symposium on Applied Computing, SAC 2011, TaiChung, Taiwan, Province of China, 21/03/11. https://doi.org/10.1145/1982185.1982225

TY - GEN

T1 - Towards discovering criminal communities from textual data

AU - Al-Zaidy, Rabeah

AU - Fung, Benjamin C.M.

AU - Youssef, Amr M.

PY - 2011

Y1 - 2011

N2 - In many criminal cases, forensically collected data contain valuable information about a suspect's social networks. An investigator often has to manually extract information from the collected text documents and enter it into a police database for further investigation with criminal network analysis tools. In this paper, we propose a method to discover criminal communities, to analyze the closeness of the members in the communities, and to extract useful information for crime investigation directly from the text documents. The proposed method, together with the implemented software tool, has received positive feedbacks from the digital forensics team of a law enforcement unit in Canada.

AB - In many criminal cases, forensically collected data contain valuable information about a suspect's social networks. An investigator often has to manually extract information from the collected text documents and enter it into a police database for further investigation with criminal network analysis tools. In this paper, we propose a method to discover criminal communities, to analyze the closeness of the members in the communities, and to extract useful information for crime investigation directly from the text documents. The proposed method, together with the implemented software tool, has received positive feedbacks from the digital forensics team of a law enforcement unit in Canada.

KW - community discovery

KW - crime investigation

KW - data mining

KW - forensic analysis

UR - https://www.scopus.com/pages/publications/79959297930

U2 - 10.1145/1982185.1982225

DO - 10.1145/1982185.1982225

M3 - Conference contribution

AN - SCOPUS:79959297930

SN - 9781450301138

T3 - Proceedings of the ACM Symposium on Applied Computing

SP - 172

EP - 177

BT - 26th Annual ACM Symposium on Applied Computing, SAC 2011

T2 - 26th Annual ACM Symposium on Applied Computing, SAC 2011

Y2 - 21 March 2011 through 24 March 2011

ER -

Towards discovering criminal communities from textual data

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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