10th International Congress on Information and Communication Technology

Authors - Ahamed Nishath S, Murugeswari R
Abstract - Researchers in the field of artificial intelligence are increasingly interested in exploring how to spot and counteract the spread of fake news. When compared to machine learning approaches, deep learning methods are superior in terms of their ability to reliably identify instances of false news. This study analyses the efficacy of various neural network topologies in the classification of news items into two distinct categories: false and real. This work takes into a hybrid model that merges both CNN and RNN layers incorporate with multi-channel mechanism, Which is the most complex model. When determining model’s overall performance, criteria such as accuracy, precision, and recall rates are taken into consideration. According to the findings, the hybrid model is able to efficiently attain a high degree of accuracy, particularly 99.16% of the target accuracy. The aforementioned results highlight the adaptability of various neural network designs in the context of distinguishing between real and false news, hence revealing key insights that have the potential to be implemented in practical scenarios involving the verification of information and the evaluation of its validity.