Matching Social Issues to Technologies for Civic Tech by Association Rule Mining using Weighted Casual Confidence

TL;DR

This paper proposes a novel weighted casual confidence measure to improve association rule mining for matching social issues with technologies in civic tech communities, facilitating better collaboration.

Contribution

It introduces a new measure called weighted casual confidence to effectively mine relevant issue-technology pairs from infrequent and biased data.

Findings

Weighted casual confidence outperforms traditional measures in relevance detection.

The method successfully identifies meaningful issue-technology associations.

Application demonstrates improved civic tech collaboration matching.

Abstract

More than 80 civic tech communities in Japan are developing information technology (IT) systems to solve their regional issues. Collaboration among such communities across different regions assists in solving their problems because some groups have limited IT knowledge and experience for this purpose. Our objective is to realize a civic tech matchmaking system to assist such communities in finding better partners with IT experience in their issues. In this study, as the first step toward collaboration, we acquire relevant social issues and information technologies by association rule mining. To meet our challenge, we supply a questionnaire to members of civic tech communities and obtain answers on their faced issues and their available technologies. Subsequently, we match the relevant issues and technologies from the answers. However, most of the issues and technologies in this questionnaire data are infrequent, and there is a significant bias in their occurrence. Here, it is difficult to extract truly relevant issues--technologies combinations with existing interestingness measures. Therefore, we introduce a new measure called weighted casual confidence, and show that our measure is effective for mining relevant issues--technologies pairs.