With the rapid expansion of social media and other digital platforms, the dissemination of false information through these channels has become widespread, leading to difficulties in deter-mining the authenticity of online content. Moreover, fact-checking through traditional means is very slow and cannot efficiently manage the enormous volume of information. This paper offers a deep learning-based misinformation detection system as a solution to this problem. According to the paper, the system utilizes various NLP (Natural Language Processing) techniques such as text cleaning, tokenization, and feature extraction in order to convert text data into a form that can be easily used for learning by deep learning algorithms. A Long Short-Term Memory (LSTM) deep learning model is used as the core-based encoding mechanism to capture the underlying semantics and contextual clues hidden in the text that help determine whether the text is genuine or fake. The system was subjected to a rigorous evaluation process using well-known labelled datasets while relying on widely-accepted performance metrics including accuracy, precision, recall, and F1-score. Results from the experiments indicate that the system proposed in this paper not only outperforms existing systems in terms of accuracy but also embodies an efficient, scalable, and trustworthy method for automatically detecting fake news.

Keywords : Fake News Detection, DL, NLP, LSTM, ML, Social Media Analysis, Semantic Analysis.

Authors : Md. Bushra , G Venkata Ravali , Sk. Akbar

Title : Detecting Misinformation using Deep Learning Techniques

Volume/Issue : 2026;3(3 (July - September))

Page No : 1 - 7