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Analyzing topics and authors in chat logs for crime investigation


Analyzing topics and authors in chat logs for crime investigation

M. A. Basher, Abdur Rahman and Fung, Benjamin C.M. (2013) Analyzing topics and authors in chat logs for crime investigation. Knowledge and Information Systems . ISSN 0219-1377

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Official URL: http://dx.doi.org/10.1007/s10115-013-0617-y


Cybercriminals have been using the Internet to accomplish illegitimate activities and to execute catastrophic attacks. Computer-Mediated Communication such as online chat provides an anonymous channel for predators to exploit victims. In order to prosecute criminals in a court of law, an investigator often needs to extract evidence from a large volume of chat messages. Most of the existing search tools are keyword-based, and the search terms are provided by an investigator. The quality of the retrieved results depends on the search terms provided. Due to the large volume of chat messages and the large number of participants in public chat rooms, the process is often time-consuming and error-prone. This paper presents a topic search model to analyze archives of chat logs for segregating crime-relevant logs from others. Specifically, we propose an extension of the Latent Dirichlet Allocation-based model to extract topics, compute the contribution of authors in these topics, and study the transitions of these topics over time. In addition, we present a special model for characterizing authors-topics over time. This is crucial for investigation because it provides a view of the activity in which authors are involved in certain topics. Experiments on two real-life datasets suggest that the proposed approach can discover hidden criminal topics and the distribution of authors to these topics.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Authors:M. A. Basher, Abdur Rahman and Fung, Benjamin C.M.
Journal or Publication:Knowledge and Information Systems
Date:8 March 2013
Digital Object Identifier (DOI):10.1007/s10115-013-0617-y
Keywords:Latent Dirichlet Allocation (LDA) Topic modeling Gibbs sampling Topic evolution Author-topics over time Cybercrime
ID Code:977221
Deposited By: Danielle Dennie
Deposited On:03 May 2013 12:45
Last Modified:18 Jan 2018 17:44


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