This study demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations.

本研究展示了一种将创新数据源（社交媒体信息）引入政府会计信息系统的方法，以支持对利益相关者和管理决策的问责制。未来的会计和审计流程将严重依赖多种形式的外生数据。作为可用于生成所需信息的技术示例，该研究将文本挖掘技术和机器学习算法应用于 Twitter 数据。该信息是作为纽约市街道清洁度的替代绩效衡量标准开发的。它利用朴素贝叶斯、随机森林和 XGBoost 对推文进行分类，说明如何使用抽样方法解决类别分布不平衡问题，并使用 VADER 情感推导公众对街道清洁度的看法。本研究还将研究扩展到另一个社交媒体平台Facebook，发现两个社交媒体平台之间的增量价值不同。然后可以将这些数据链接到政府会计信息系统以评估成本并更好地了解运营的效率和有效性。