Increasing the Effectiveness of Fake News Detection: An Educational Program for High School Students Using Interactive Neural Network Training and Collective Intelligence

Abstract

This paper presents the project results designed to provide high school students with essential ICT tools to identify and counteract fake news and disinformation commonly found on the Internet, especially on platforms like X/Twitter. Additionally, it introduces an educational program that utilizes interactive neural network training and collective intelligence to combat fake news. For this project, there was developed an IT framework enabling the collective training of a specialized neural network. In order to conduct a quasi-experiment, we engaged three research groups of high school students, each containing app. 10-15 members. Through a set of comprehensive workshops, the students were trained to recognize harmful online content. After this training, students actively participated in data classification on various topics, laying the foundation for the neural network's training model. The presented results underscore the efficacy of this immersive method in imparting digital literacy and enhancing the group intelligence. Moreover, the results highlight the promising potential of machine learning in assisting youth to navigate the complex digital terrain safely and responsibly. The final phase of the conducted research involved testing the trained neural network in detecting disinformation, particularily in the topics of 5G technologies and immigration problems in Poland.