Detection of Fake News Stance Employing Swarm Intelligence Based Feature Extraction and Ensemble Deep Learning
Keywords:
Fake news, preprocessing, Modified Binary Dragonfly Optimization algorithm (MBDFO), Fuzzy Particle Swarm Optimization (FPSO), Ensemble Learning Model (ELM), fake news detectionAbstract
Recently, the internet being easily accessible has helped the people in finding and consuming news through social platforms owing to its reduced expense, simplicity of access, and rapid transfer of information. It is possible for the users to publish and share various kinds of information in every form just with one click of a button. Due to their detrimental effects on society and the nation, the dissemination of false information through social media and other platforms has given birth to frightening circumstances. Although MLTs (machine learning techniques) detect fake news contents in social platforms, they are complex issues that are challenging due to changing fake news contents that are presented on the internet. In this technical study, a methodology for identifying fake news using intelligent feature extraction and ensemble-based classifiers is suggested to address the aforementioned issue. This recommended approach uses a four-step process to spot fake news on social media. The dataset is initially pre-processed in the approach to turn unorganized data sets into sorted data sets. The second stage, which employs the MBDFO (Modified Binary Dragonfly Optimization) algorithm, is brought on by the varying linkages between news pieces and the unknown features of false news. d on FPSO (fuzzy particle swarm optimization) is presented in the third phase to carry out the feature reduction operation. At last, in this research work, an ELM (Ensemble Learning Model) is built for learning how the news articles are represented and the fake news detection is carried out effectively. The reasoning is developed in this research by getting a dataset from kaggle. The results achieved prove that the proposed system is effective.
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