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    Social Data Mining for Crime Intelligence: Contributions to Social Data Quality Assessment and Prediction Methods

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    PhD Thesis (7.657Mb)
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    Publication date
    2017
    Author
    Isah, Haruna
    Supervisor
    Neagu, Daniel
    Trundle, Paul R.
    Keyword
    Social networks analysis
    Data mining
    Social network data quality
    Digital crime intelligence
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    Department of Computer Science
    Awarded
    2017
    
    Metadata
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    Abstract
    With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems.
    URI
    http://hdl.handle.net/10454/16066
    Type
    Thesis
    Qualification name
    PhD
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    Theses

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