Explainable multi-task convolutional neural network framework for electronic petition tag recommendation

Abstract

Electronic petition (e-petition) is an electronic government (e-government) service that allows citizens to file petitions to governments via the internet. The complexity of the e-petition filing process and the unexplainable e-government tools would reduce the perceived ease of use and trust of users, which causes citizens to make errors when tagging their e-petition. These errors may lead to failure and delay in resolving the e-petition, which further restrains citizen engagement and electronic participation (e-participation) adoption on e-petition platforms (EPP). The purpose of this study is to develop an explainable tag recommender system to assist citizens in tagging their e-petitions. Specifically, we design an explainable multi-task learning framework for e-petition tag recommendation based on convolutional neural networks and layer-wise relevance propagation. We also conduct both quantitative and qualitative experiments to demonstrate the recommendation effectiveness as well as interpretability of our model. This is among the first attempt to design an e-petition tag recommender system and an explainable e-government recommender system. The practical implications of our research are two-fold. For citizens, our proposed model recommends more accurate tags with human-understandable explanations, which could assist citizens’ tagging decisions and increase the possibility for an e-petition to be resolved. For governments, the e-petition service quality of governments would be enhanced, which further promotes e-participation adoption, citizen engagement, and e-government success on EPPs.