Digital Threat: Misinformation Detection using LSTM in TensorFlow

Abstract

In today's digital world, where false information spreads swiftly via social media and jeopardizes public trust, misinformation has become a significant issue. Despite the existence of some traditional detection methods, they are barely able to keep up with the evolving deception techniques and sophisticated manipulation techniques. In order to solve these kinds of problems, every paper develops a progress detection system based on bidirectional long short-term memory networks and their attention tool. Our system's coverage of both real and fake news was very wide because it was built on two large data sets that included roughly 45,000 news articles from numerous sources. We used a rigorous data preprocessing step to process our data, which involved tokenization, text removal, and the implementation of a custom LSTM architecture. In order to improve each article's ability to distinguish between real news and false information, the attention mechanism also helps the model focus on the most relevant portion. The outcomes were striking. Our model achieved a precision and recall score of over 99.8% and an accuracy of 99.88% on test data. A score of 1.0000 under the AUC- ROC indicates high discrimination of news categories, and the confusion matrix shows a few instances of misclassification. According to this data, deep learning techniques—particularly bidirectional LSTMs with attention—can also offer effective ways to combat false information. With references to automated fact- checking systems and social media monitoring, this model's high performance can be interpreted as a sign that it can be applied in practical situations. The current study provides practical solutions for maintaining information integrity in a world that is becoming more interconnected and where news accuracy must be confirmed.