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

TY - JOUR

T1 - Enhancing the government accounting information systems using social media information

T2 - An application of text mining and machine learning

AU - Duan, Huijue Kelly

AU - Vasarhelyi, Miklos A.

AU - Codesso, Mauricio

AU - Alzamil, Zamil

N1 - Publisher Copyright: © 2022 Elsevier Inc.

PY - 2023/3

Y1 - 2023/3

N2 - 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.

AB - 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.

KW - Machine learning

KW - Sentiment analysis

KW - Social media

KW - Text mining

UR - https://www.scopus.com/pages/publications/85142690091

UR - https://www.scopus.com/pages/publications/85142690091#tab=citedBy

U2 - 10.1016/j.accinf.2022.100600

DO - 10.1016/j.accinf.2022.100600

M3 - Article

SN - 1467-0895

VL - 48

JO - International Journal of Accounting Information Systems

JF - International Journal of Accounting Information Systems

M1 - 100600

ER -