What question did this study set out to answer?

The research aims to develop an automated framework to effectively detect fake news, addressing limitations in existing methods.

February 12, 2026

An Automated Fake News Detection Framework Using Residual Convolutional Bi-LSTM with Improved Optimisation-Based Weighted Feature Representation

Key Points

The research aims to develop an automated framework to effectively detect fake news, addressing limitations in existing methods.
Utilized publicly available datasets for training and testing.
Performed data preprocessing to clean information.
Applied BERT for initial feature extraction, followed by RNN and CNN for temporal and spatial features.
Implemented the Improved Horse Herd Optimisation Algorithm for feature weight tuning.
Used Residual Convolutional Bi-LSTM for final detection.
The proposed framework showed a performance improvement of over 22.9% compared to DNN methods.
Achieved 8.7% better results than CNN methods.
Outperformed LSTM and Bi-LSTM methods by 17.6% and 7% respectively using BERT features.

Abstract

In the extensive growth of social media platforms, fake news is false information spread among people around the world. Fake news weakens public trust and societal stability; it has negative impacts that necessitate for fake news. In order to enhance the public trust and improve the social media users, fake news detection is crucial, which is a complex and multi-dimensional task due to the diverse characteristics of fake news. More researchers have explored several deep learning and machine learning approaches for fake news detection approaches yet the existing binary classification mechanism is not sufficient for handling imbalanced training data to enhance poor performance in the fake news detection process also it affects the interpretability and consumes high computational duration. With the aim of rectifying these complexities, an automated fake news detection mechanism is proposed in this research work to ultimately recognise unreliable information for improving social media users’ effectiveness. Initially, the necessary data are taken from the publicly available datasets, such as the WELFake dataset and Fake News dataset; it is fed into the preprocessing phase. Here, the punctuation, special characters, redundant content and inappropriate data are mitigated, which can generate cleaned data. Then, the preprocessed data is given to the feature extraction process to efficiently extract the relevant informative features for enhancing better detection outcomes. Here, the Bidirectional Encoder Representations from Transformers (BERT) is initially used to retrieve the first set of features, the Recurrent Neural Network (RNN) extracts the temporal features and the Convolution Neural Network (CNN) is used to extract the spatial features. Subsequently, these features are fused in the weighted feature selection process, where the weights are quickly tuned through the Improved Horse Herd Optimisation Algorithm (IHHOA). Further, it is passed to the Residual Convolution Bidirectional Long Short Term Memory (RConv-Bi-LSTM) technique for significantly detecting fake news in a limited duration. The entire detection process is estimated with several performance metrics and contrasted over various conventional methods to generate optimal outcomes. Here, the designed framework’s FM value (dataset 1) is enhanced over 22.9% DNN, 8.7% CNN, 17.6% LSTM and 7% Bi-LSTM methods (BERT features) to show its better performance over the existing approaches.

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