A Machine Learning and Deep Learning Based Approach to Detect Inaccurate Health Information in Bengali Language

Abstract

The spread of false health news and its dis-semination on the internet has become a major concern, due to its potential to have disastrous effects. To detect it, numerous approaches have been attempted. However, we are aware of very few studies that have sought to identify health related false information in Bangla. In this study, we have analyzed the performance of various Machine Learning and Deep Learning approaches in detecting Bangla health-related misinformation that is available online. We have created a comprehensive data repository, consisting more than 5000 data, manually annotated to two fixed categories. Several supervised machine learning classifiers and Deep Learning algorithms have been employed in this experiment to detect fake health news at the textual level. Our experiment achieves maximum accuracy of 88% in the Passive Aggressive approach and 89% in the Bi-LSTM approach. We believe that our dataset is a significant collection of health-related data in Bangla. It may open up new perspectives for the analysis of Bangla-language and health misinformation detection.