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dc.contributor.advisorNeagu, Daniel
dc.contributor.advisorTrundle, Paul R.
dc.contributor.authorIsah, Haruna*
dc.date.accessioned2018-05-30T10:21:59Z
dc.date.available2018-05-30T10:21:59Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10454/16066
dc.description.abstractWith 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.en_US
dc.description.sponsorshipCommonwealth Scholarship Commission.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectSocial networks analysisen_US
dc.subjectData miningen_US
dc.subjectSocial network data qualityen_US
dc.subjectDigital crime intelligenceen_US
dc.titleSocial Data Mining for Crime Intelligence: Contributions to Social Data Quality Assessment and Prediction Methodsen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentDepartment of Computer Scienceen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2017
refterms.dateFOA2018-07-29T02:24:44Z


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