Twitter Truth: Advanced Multi-Model Embedding for Fake News Detection

The identification of fake news represents a substantial challenge within the context of the accelerated dissemination of digital information, most notably on social media and online platforms. This study introduces a novel approach, entitled " MT-FND: Multi-Model Embedding Approach to Fake News Detection," which is designed to enhance the detection of fake news. The methodology presented here integrates the strengths of multiple transformer-based models, namely BERT, ELECTRA, and XLNet, with the objective of encoding and extracting contextual information from news articles. In addition to transformer embeddings, a variety of other features are incorporated, including sentiment analysis, tweet length, word count, and graph-based features, to enrich the representation of textual content. The fusion of signals from diverse models and features provides a more comprehensive and nuanced comprehension of news articles, thereby improving the accuracy of discerning misinformation. To evaluate the efficacy of the approach, a benchmark dataset comprising both authentic and fabricated news articles was employed. The proposed framework was tested using three different machine-learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB). The experimental results demonstrate the effectiveness of the multi-model embedding fusion approach in detecting fake news, with XGB achieving the highest performance with an accuracy of 87.28%, a precision of 85.56%, a recall of 89.53%, and an F1-score of 87.50%. These findings signify a notable improvement over traditional machine learning classifiers, underscoring the potential of this fusion approach in advancing methodologies for combating misinformation, promoting information integrity, and enhancing decision-making processes in digital media landscapes.