Sentiment analysis and topic modeling in the context of scientific integrity: implications for teaching and research in higher education - GaoFeng Han; Fang Xia (Discover Applied Sciences)
Abstract In the contemporary digital era, upholding research integrity assumes paramount importance for the reputation of higher education institutions and the advancement of science. This study undertakes an interdisciplinary computational investigation into the emotional response of the public to issues pertaining to research integrity, and the subsequent impact on social cognition. The study has constructed a database comprising 58,729 text data points, which have been sourced from social media (Twitter and Weibo), mainstream media reports, academic literature, and public commentary. The application of topic modeling using Latent Dirichlet Allocation (LDA) has enabled the identification of three core topics: “academic integrity issues,” “research governance and institutional transparency,” and “research ethics and accountability. “The implementation of sentiment analysis using the BERT deep learning model has quantified the presence and intensity of emotional reactions. The results obtained demonstrate that the public expresses significant concern and experiences significant negative emotions in relation to academic integrity issues, particularly on social media. Conversely, public discourse on research governance and institutional transparency is predominantly positive, while discussions on research ethics and accountability are more nuanced. These findings underscore the necessity to consider the diversity and dynamics of public emotions when formulating research integrity management strategies, providing novel insights for understanding and addressing the research integrity crisis and emphasizing the importance of sentiment analysis in strategy formulation.