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Monitoring Public Participation in Multilateral Initiatives Using Social Media Intelligence

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Data Science and Big Data Analytics

Abstract

Governments, multilateral agencies like the World Bank, United Nations, and Development Banks as well as other nonprofits are involved in a variety of developmental activities across the world. A lot of resources are spent to ensure proper consultations and post-implementation verification of results. But this does not completely ensure whether the objectives are achieved. The new web technologies provided methodologies and developed tools that allow the users to pool resources on projects over the Internet. Social media allowed real-time feedback for citizens, monitoring developmental initiatives of Governments and multilateral agencies. The role of technology ensures that the consultations and ongoing feedback can be captured, analyzed, and used in understating the stakeholder reactions to the project and its implementation. This helps in making necessary course corrections avoiding costly mistakes and overruns. In this paper, we model a tool to monitor, study, and analyze popular feedback, using forums, social media, surveys, and other crowdsourcing techniques. The feedback is gathered and analyzed using both quantitative and qualitative methods to understand what crowd is saying. The summation and visualization of patterns are automated using text mining and sentiment analysis tools including text analysis and tagging/annotation. These patterns provide insight into the popular feedback and sentiment effectively and accurately than the conventional method. The model is created by integrating such feedback channels. Data is collected and analyzed, and the results are presented using tools developed in open-source platform.

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Correspondence to Ulanat Mini .

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Mini, U., Nair, V., Poulose Jacob, K. (2019). Monitoring Public Participation in Multilateral Initiatives Using Social Media Intelligence. In: Mishra, D., Yang, XS., Unal, A. (eds) Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-10-7641-1_17

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