A BERT-based Approach to Alleviate Civic Tech Tools Overcrowding: A case study of Taiwan's JOIN e-petition system

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Abstract
Online petition systems as a crowd-sourcing and feedback tool offered by public authorities are becoming more and more common these days. The volume of incoming petitions from digital platforms is increasing. Governments typically review petitions in a screening process before they can be posted on an online platform. In order to reduce the burden on public authorities and to provide fast feedback to a petitioner on whether a petition is admissible or not, an automated system is proposed to assistance to the authorities and petitioners alike. The proposed system attempts to classify whether a petition is admissible or not. By applying a BERT-based model to a dataset of petitions submitted to Taiwan’s JOIN platform 2015-2023, the classification model achieve a classification accuracy of 67%. While the model would be improved in future work, the experiments conducted the overall feasibility of the proposed system to alleviate the overcrowding of civic tech tools.

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