2,900 research outputs found
Fake News Detection Through Multi-Perspective Speaker Profiles
Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles
X-CapsNet For Fake News Detection
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
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