Home // IMMM 2019, The Ninth International Conference on Advances in Information Mining and Management // View article

Fake News Detection Method Based on Text-Features

Authors:
Ahlem Drif
Zineb Ferhat Hamida
Silvia Giordano

Keywords: Fake news detection; social networks; deep learning; convolutional neural network; text classification; words embedding technique.

Abstract:
Feature extraction is a critical task in fake news detection. Embedding techniques, such as word embedding and deep neural networks, are attracting much attention for textual feature extraction, and have the potential to learn better representations. In this paper, we propose a joint Convolutional Neural Network model (CNN) and a Long Short Term Memory (LSTM) recurrent neural network architecture, taking advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via LSTM. An empirical evaluation of our model shows good prediction accuracy of fake news detection, when compared to Support Vector Machine and CNN baselines.

Pages: 26 to 31

Copyright: Copyright (c) IARIA, 2019

Publication date: July 28, 2019

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-731-3

Location: Nice, France

Dates: from July 28, 2019 to August 2, 2019