Abstract
Environmental engineering plays a crucial role in addressing the sustainability challenges of the environment and everyday life. These areas cover a wide range of aspects, from environmental impacts, efficient use of natural resources, waste management and recycling, and improving air and water quality to preventing, managing and mitigating environmental hazards and disasters. For example, climate change, global warming, pollutant emissions, etc., are frequently discussed on social media. Unfortunately, however, these platforms, as the primary means of mass information, provide scope for misinformation, manipulation and influencing opinions through dubious or outright fake news and posts. Despite the prevalence and growing volume of research on detecting fake news, there is a significant lack of quality publications focusing specifically on this environmental engineering-related area. In this paper, the authors aim to bridge this gap by developing a supervised machine-learning model for detecting fake news in this field of science. To build a robust model, a training dataset was constructed by using a specific dataset of labelled social media posts containing keywords from Chemical Engineering Transactions journal articles, with a particular focus on sustainability and environmental aspects. This research contributes to enhancing the integrity of environmental engineering in social media discourse by providing a tool for identifying and mitigating the spread of misinformation, promoting informed decision-making and critical thinking, and improving public awareness of environmental matters.
