Fake News Detection Using Semantic Analysis and Machine Learning Techniques

In the era of online information dissemination, the challenge of fake news and disinformation has become increasingly severe, making reliable detection crucial for maintaining the integrity of information ecosystems. Using semantic analysis and several forms of machine learning, the authors of this research study provide an innovative method for identifying instances of false news. To describe the contextual semantics of textual data more precisely, we use Word Embeddings and Long Short-Term Memory (LSTM) networks. From multiple internet sources, we gathered a dataset of real and false news stories, which we then preprocessed using tokenization, stopword removal, stemming, and sequence padding. To capture the semantic links between words and phrases, we employed the Global Vectors for Word Representation (GloVe) to convert each word into a dense vector representation. This was used to feed a stacked LSTM model, a kind of RNN that can recognise long-distance relationships in text data. Our suggested approach beat conventional models such as Support Vector Machines (SVM), Naive Bayes, Convolutional Neural Networks (CNN), and LSTM without Word Embeddings during rigorous testing. Our method achieved an accuracy of 0.92, demonstrating the effectiveness of combining LSTM networks with Word Embeddings for fake news detection. Despite these promising results, challenges related to language generalization, sensitivity to word embedding quality, and inclusion of additional sources of information remain. Our research signifies a key contribution to the ongoing fight against fake news and disinformation, providing an effective approach to detect fake news with potential for further enhancement and application in real-world scenarios. Future work could focus on fine-tuning the model for different languages and domains, improving word embeddings, and integrating additional information sources to further improve fake news detection.