Enhancing the government accounting information systems using social media information: An application of text mining and machine learning

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 algo-rithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naive 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 ex-tends 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 gov-ernment accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations.