An optimal detection of fake news from Twitter data using dual-stage deep capsule autoencoder

Abstract

ABSTRACT The proposed work introduces a dual-stage deep capsule autoencoder (DSDC-AE) model for fake news detection on Twitter data. Initially, the input Twitter data are pre-processed using tokenisation, stemming, stop word removal and lemmatisation. The text features are extracted from the pre-processed data using Improved Term Frequency Inverse Document Frequency (ITF-IDF), Unigrams, Bigrams, Enhanced Bag of Words (EBoW) and Advanced Word2vec (Word2 Vector). To minimise the large feature dimensionality, the proposed work uses the Horse Herd Optimisation algorithm (HOA) for the feature selection. Finally, the selected features are subjected to the proposed classifier, in which the fake news from Twitter data is detected and classifies the given input data as real or fake. The proposed DSDC-AE model uses three datasets for the analysis: fake news detection on Twitter EDA, ISOT dataset and FakeNewsNet dataset. The mentioned dataset obtains the accuracy of 99.52%, 99.51% and 99.47%.