X-CapsNet For Fake News Detection

TL;DR

This paper introduces X-CapsNet, a transformer-based Capsule neural network model that effectively detects fake news of varying lengths by combining dynamic routing, size-based classifiers, and indirect feature representations, outperforming existing methods.

Contribution

The paper presents a novel X-CapsNet architecture integrating CapsNet with size-based classifiers and indirect features for improved fake news detection across different statement lengths.

Findings

X-CapsNet outperforms state-of-the-art baselines on Covid-19 and Liar datasets.

The model effectively detects both short and long fake news statements.

Using indirect features improves short news statement detection accuracy.

Abstract

News consumption has significantly increased with the growing popularity and use of web-based forums and social media. This sets the stage for misinforming and confusing people. To help reduce the impact of misinformation on users' potential health-related decisions and other intents, it is desired to have machine learning models to detect and combat fake news automatically. This paper proposes a novel transformer-based model using Capsule neural Networks(CapsNet) called X-CapsNet. This model includes a CapsNet with dynamic routing algorithm paralyzed with a size-based classifier for detecting short and long fake news statements. We use two size-based classifiers, a Deep Convolutional Neural Network (DCNN) for detecting long fake news statements and a Multi-Layer Perceptron (MLP) for detecting short news statements. To resolve the problem of representing short news statements, we use indirect features of news created by concatenating the vector of news speaker profiles and a vector of polarity, sentiment, and counting words of news statements. For evaluating the proposed architecture, we use the Covid-19 and the Liar datasets. The results in terms of the F1-score for the Covid-19 dataset and accuracy for the Liar dataset show that models perform better than the state-of-the-art baselines.