X-CapsNet For Fake News Detection
Mohammad Hadi Goldani Reza Safabakhsh Saeedeh Momtazi
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.