Corporate social responsibility disclosure prediction using LSTM neural network

Corporate social responsibility (CSR) has gained a great deal of interest in recent years due to the need for information that can help many stakeholders (e.g., governments, investors, professional organizations, researchers, etc.) understand companies' contributions to the environment and society. CSR disclosure (CSRD) is now the key source of such information when analyzing, for example, an institution's future performance. In the current body of CSRD literature, the majority of quantitative CSRD studies have relied on traditional statistical approaches for the correlation analysis of CSRD influencing factors. In this paper, we intend to quantitatively analyze firms' characteristics related to CSRD in Saudi Arabia, understand CSRD and its influencing factors, and predict CSRD patterns. This study lays the groundwork to help companies make informed decisions. It also helps many other stakeholders better understand CSRD's impacts. To achieve this, we propose a deep learning framework based on long short-term memory (LSTM) for identifying and predicting CSRD patterns. Moreover, a correlation-based technique is also used to visualize the relationships between variables and identify the significant features. The dataset used in this study was collected from annual reports, CSR reports, and firms' websites between 2015 and 2018. It contains a variety of variables to explain the CSR behaviour of 117 companies. The proposed framework is evaluated with several approaches, including logistic regression (LR), K-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and decision trees (DT). Compared to other machine learning models, experiment results show that LSTM achieved acceptable results with the highest accuracy of 88%.