International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Recent years have seen a significant and widespread rise in false news, which is defined as material that is shared with the intention of defrauding people.This kind of misinformation is dangerous to social cohesion and wellbeing because it exacerbates political polarisation and public mistrust of authority figures.As a result, fake news is a phenomena that significantly affects our social lives, especially in politics.In order to address this issue, this study suggests brand-new methods based on machine learning (ML) and deep learning (DL) for the fake news identification system.This paper's primary goal is to identify the best model that produces high accuracy performance.Hence, in order to identify fake news, we provide an improved Convolutional Neural Network model (OPCNN-FAKE).Using four benchmark datasets for fake news, we assess how well OPCNN-FAKE performs in comparison to Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and The Six Regular ML Techniques: Decision Tree (DT), logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB). The parameters of ML and DL have each been optimised using the grid search and hyperopt optimization approaches, respectively. Moreover, Glove word embedding has been utilised to encode features as a feature matrix for DL models while N-gram and Term FrequencyInverse Document Frequency (TF-IDF) have been used to extract features from the benchmark datasets for regular ML. Accuracy, precision, recall, and F1- measure were used to validate the data in order to assess the performance of the OPCNN-FAKE. Compared to other models, the OPCNN-FAKE model has the best performance for each dataset.

Keywords: Fake News Detection, OPCNN-FAKE, Deep Learning, Natural Language Processing, BERT, Misinformation, Text Classification

Downloads: | DOI: 10.17148/IJARCCE.2025.14735