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Automated Identification of Child Abuse in Chat Rooms by Using Data Mining
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Author(s): Mohammadreza Keyvanpour (Alzahra University, Iran), Mohammadreza Ebrahimi (Concordia University, Canada), Necmiye Genc Nayebi (École de Technologie Supérieure (ÉTS), Canada), Olga Ormandjieva (Concordia University, Canada)and Ching Y. Suen (Concordia University, Canada)
Copyright: 2016
Pages: 30
Source title:
Data Mining Trends and Applications in Criminal Science and Investigations
Source Author(s)/Editor(s): Omowunmi E. Isafiade (University of Cape Town, South Africa)and Antoine B. Bagula (University of the Western Cape, South Africa)
DOI: 10.4018/978-1-5225-0463-4.ch009
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Abstract
Providing a safe environment for juveniles and children in online social networks is considered as one of the major factors of improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of child abuse in cyber space has become inevitable. Using automatic ways to combat this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and pattern classification in machine learning. This chapter covers different data mining methods including preprocessing, feature extraction and the popular ways of feature enrichment through extracting sentiments and emotional features. A brief tutorial on classification algorithms in the domain of automated predator identification is also presented through the chapter. Finally, the discussion is summarized and the challenges and open issues in this application domain are discussed.
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