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.
