Stance detection using improved whale optimization algorithm

Avinash Chandra Pandey,Vinay Anand Tikkiwal

doi:10.1007/s40747-021-00294-0

Avinash Chandra Pandey, Vinay Anand Tikkiwal

Open Access

https://doi.org/10.1007/s40747-021-00294-0

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Abstract

News is a medium that notifies people about the events that had happened worldwide. The menace of fake news on online platforms is on the rise which may lead to unwanted events. The majority of fake news is spread through social media platforms, since these platforms have a great reach. To identify the credibility of the news, various spam detection methods are generally used. In this work, a new stance detection method has been proposed for identifying the stance of fake news. The proposed stance detection method is based on the capabilities of an improved whale optimization algorithm and a multilayer perceptron. In the proposed model, weights and biases of the multilayer perceptron are updated using an improved whale optimization algorithm. The efficacy of the proposed optimized neural network has been tested on five benchmark stance detection datasets. The proposed model shows better results over all the considered datasets. The proposed approach has theoretical implications for further studies to examine the textual data. Besides, the proposed method also has practical implications for developing systems that can result conclusive reviews on any social problems.

Highlights

News is a medium that keeps everyone updated about the events that have taken place
Davis and Proctor [21] presented three different approaches for correctly identifying the stances of the news; the first approach is based on bag-of-words with a three-layer multilayer perceptron (BoW-MLP), the second is a bag of words with bidirectional LSTM (BoW-BiLSTM), and the third is a bag of words with a concatenated multilayer perceptron (BoW-CMLP)
The results show that the bag of vector technique performs well with neural network for related and agree news articles

Summary

Introduction

News is a medium that keeps everyone updated about the events that have taken place. It has two parts, one is the headline of the news and the other is the content body. To discover the stance of fake news, many models such as traditional stance, machine learning, deep learning, and natural language processing (NLP)-based models have been proposed in the literature. The traditional stance-based models compare the body and headlines to check the credibility of news [27]. Shu et al [71] presented an NLP-based approach for identifying the stance of fake news. The deep neural network-based models extract useful features automatically from datasets using backpropagation [63]. An improved whale optimization algorithm (IWOA) has been proposed for optimizing the hyper-parameters of neural networks. The proposed model uses the word embedding technique for normalizing the textual data followed by an optimized neural network to get the stance. – The proposed IWOA method are used for optimizing the hyper-parameters of neural network and to identify the stance of textual data. The section “Evaluating IWOA for bias(es)” evaluates for the bias(es) and the section “Experimental results” reports the Experimental outcomes followed by the conclusion in the section “Conclusion and future work”

Background study

Experimental results

Methods

Conclusion and future work