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  1. eCrime Researchers Summit
  2. 2010 eCrime Researchers Summit
  3. Authorship attribution of web forum posts
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2015 APWG Symposium on Electronic Crime Research (eCrime)
2014 APWG Symposium on Electronic Crime Research (eCrime)
2012 eCrime Researchers Summit
2011 eCrime Researchers Summit
2010 eCrime Researchers Summit
Copyright page
Program agenda
Table of content
Authorship attribution of web forum posts
A framework for financial botnet analysis
Koobface: The evolution of the social botnet
NoTabNab: Protection against the “tabnabbing attack”
Automatically determining phishing campaigns using the USCAP methodology
Feature selection for Spam and Phishing detection
On SCADA control system command and response injection and intrusion detection
Securing IS assets through hacker deterrence: A case study
2009 eCrime Researchers Summit
2008 eCrime Researchers Summit

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Authorship attribution of web forum posts

Content Provider IEEE Xplore Digital Library
Author Pillay, S.R. Solorio, T.
Copyright Year 2010
Description Author affiliation: Department of Computer and Information Sciences at the University of Alabama at Birmingham, 1300 University Boulevard, 139 Campbell Hall, 35294 USA (Pillay, S.R.; Solorio, T.)
Abstract Extracting useful information from user generated text on the web is an important ongoing research in natural language processing, machine learning, and data mining. Online tools like emails, news groups, blogs, and web forums provide an effective communication platform for millions of users around the globe and also provide an added advantage of anonymity. Millions of people post information on different web forums daily. The possibility of exchanging sensitive information between anonymous users on these web forums cannot be ruled out. This document proposes a two stage approach for combining unsupervised and supervised learning approaches for performing authorship attribution on web forum posts. During the first stage, the approach focuses on using clustering techniques to make an effort to group the data sets into stylistically similar clusters. The second stage involves using the resulting clusters from stage one as features to train different machine learning classifiers. This two stage approach is an effort towards reducing the complexity of the classification task and boosting the prediction accuracy.
Starting Page 1
Ending Page 7
File Size 447257
Page Count 7
File Format PDF
ISBN 9781424477609
ISSN 21591245
e-ISBN 9781424477623
DOI 10.1109/ecrime.2010.5706693
Language English
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date 2010-10-18
Publisher Place USA
Access Restriction Subscribed
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword Training stylometry Accuracy Machine learning algorithms Machine learning Feature extraction text categorization Authorship attribution machine learning classifiers clustering Classification algorithms Classification tree analysis
Content Type Text
Resource Type Article
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