A Prototype for mapping of tweets on state services for decision support: a case of Huduma Kenya / Ng’ang’ira, Judy Nyakairu
Publication details: Nairobi Strathmore University 2018Description: xii,57p ill.colSubject(s): LOC classification:- QA76.76.N43 2018
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
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Thesis | Strathmore University (Main Library) Special Collection | QA76.76.N43 2018 | Not for loan | 75 |
The growing public participation in decision making regarding the management of State resources demands for a tool that support meaningful insight of the many aspects on environmental issues, for the development and evaluation of alternative management options. Twitter has become quite popular among researchers due to its massive volume in data thus drawing a great interest by the public service community to answer questions relating to the use and misuse of public service offices. However, despite the growing participation by the researchers using twitter as a public service misuse detection the State does not seem to optimize the opportunity that twitter offers for detecting and monitoring the services offered at Huduma Kenya, a one stop shop offering a variety of state services in almost all the counties. The main objective of this research was to demonstrate that twitter tweets can be dependably grouped based on state services selected keywords. The magnitude of state service tweets can be predicted with high accuracy. The method used includes various steps that can be summarized as first categorizing the groups on twitter and defining them. Second, finding out how each group pattern of activity contributes value in group participation. Thirdly, the identified users were invited to contribute in the interviews. Fourth, analysis of the interview results was carried out enabling the researcher to identify findings of ill-structured decisions in state services. Also, a mixture of related investigation similarity diagramming and grounded theory techniques were used to identify different benefit-related trends, patterns, and evolving relationships through all interviewees. Then, the data was sorted and compared by group type to discover which themes were most repetitively related per group. Moreover, to estimate on the generalization of these results to the user population at large, access usage logs was required to determine usage levels
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