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A Social Media Mining and Analysis Approach for Supporting Cyber Youth Work
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Author(s): W.M. Wang (Knowledge Management and Innovation Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong), Benny C.F. Cheung (Knowledge Management and Innovation Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong), Zeno C.S. Leung (Centre for Third Sector Studies, Department of Applied Social Sciences, The Hong Kong Polytechnic University Hong Kong, Hong Kong), K.H. Chan (Department of Industrial and Systems Engineering,The Hong Kong Polytechnic University, Hong Kong)and Eric W.K. See-To (Knowledge Management and Innovation Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong)
Copyright: 2019
Pages: 17
Source title:
Multigenerational Online Behavior and Media Use: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-7909-0.ch092
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Abstract
Cyber youth work is a pioneering and proactive approach used by non-government organizations to address the changing needs of the youth groups, particularly those at-risk and “hidden” young people. This paper describes the development process of a social media mining and analysis method, which is built to facilitate services for cyber youth work. The method incorporates an iterative method for collecting, selecting and extracting domain keywords in selected social media. A hybrid approach which combines heuristic rules and n-gram analysis for bilingual word segmentation has been developed. Then, social network analysis is used to analyze the extracted results. A pilot study has been done by using drug abuse as the study topic. It demonstrated high potential of the method to enhance cyber youth work. The weights deduced by the method have found to have a positive correlation with the benchmarked scores. It helps to understand the characteristic of the network, identify target clients, and provide data support for marking decisions.
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