Abstract
Social Media Analytics (SMA) can provide methods and tools for government to monitor, analyse and visualise social media data, and assist with Citizen Relationship Management (CzRM) and decision making. However, social media presents an explosion of unstructured Big Data, leading to many challenges for government relating to the processes of SMA and CzRM. The purpose of this paper is to report on a Twitter Dataset Analysis conducted to evaluate SMA methods and tools and the extent to which they are useful for supporting CzRM. Twitter data from South Africa and Germany was used in the dataset. The findings revealed that the proposed SMA methods and three tools (NVivo, uClassify and PowerBI) were useful for collecting, analysing and visualising social media data. NVivo successfully collected Tweets from South Africa and Germany over a four-week period. These Tweets were analysed for negative and positive sentiments using uClassify and visualised for insights using PowerBI. The visualisations were useful for determining trends and insights into citizens’ views and issues from their posts. The main contribution is the extended framework of SMA methods and techniques that was used to guide the dataset analysis. A practical contribution is the demonstration of the framework of SMA methods and techniques in terms of utility and usability in a real-world context.
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Yakobi, K., Scholtz, B. (2022). Methods and Tools for Social Media Analytics to Support Citizen Relationship Management: A Dataset Analysis of Tweets from Germany and South Africa. In: Papagiannidis, S., Alamanos, E., Gupta, S., Dwivedi, Y.K., Mäntymäki, M., Pappas, I.O. (eds) The Role of Digital Technologies in Shaping the Post-Pandemic World. I3E 2022. Lecture Notes in Computer Science, vol 13454. Springer, Cham. https://doi.org/10.1007/978-3-031-15342-6_10
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