@inproceedings{10.1145/3560107.3560201,
author = {Melo, William Maximiliano Carvalho de and Costa, Ana and Cambra, Paulo},
title = {Policy Radar: Creation of a tool for monitoring Planning Instruments in Portugal},
year = {2022},
isbn = {9781450396356},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3560107.3560201},
doi = {10.1145/3560107.3560201},
abstract = {Planning Instruments (PI) are textual documents in the form of plans or strategies that articulate public policies with objectives and goals by the actions of public authorities. PIs offer a large volume of textual information that can change from time to time. Each PI can contain hundreds of objectives and goals with its own indicators, execution rates, and time limits. The volume of PI's information makes it difficult to monitor the execution of all plans and carry out cross-sectional analyses to perceive parallel activities and possible synergies in addressing public policy problems. The present study seeks to respond to the challenge of systematizing these PIs with a dual purpose: On the one hand, it aims to develop a decision support system that allows policymakers to monitor the execution of the different IPs and identify areas with potential for convergence. At the same time, in an open electronic governance model, the system is intended to be available on a public portal, where citizens and stakeholders can research and follow the various public policy indicators. The project will build an algorithm based on natural language processing (NLP) and machine learning. Through text mining, the algorithm will learn how to extract, categorize and compare information from different PIs, such as operational objectives, goals, and execution rates. The last step will be to feed a search engine that will simplify the navigation among other PIs.},
booktitle = {Proceedings of the 15th International Conference on Theory and Practice of Electronic Governance},
pages = {579–581},
numpages = {3},
keywords = {E-Governance, Machine Learning, Planning Instruments, Policymaking},
location = {Guimar\~{a}es, Portugal},
series = {ICEGOV '22}
}

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