(2021). Deep Learning for Fake News Detection in a Pairwise Textual Input Schema. Computation, 9(2), 20. https://doi.org/10.3390/computation9020020
Περίληψη
In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.
- DOI
- 10.3390/computation9020020
- Τύπος
- Άρθρο σε Περιοδικό
- Έτος
- 2021
Σύνδεσμοι
BibTeX
@article{mouratidis2021deep,
title = {Deep Learning for Fake News Detection in a Pairwise Textual Input Schema},
author = {Despoina Mouratidis and Maria Nefeli Nikiforos and Katia Lida Kermanidis},
url = {https://doi.org/10.3390/computation9020020},
doi = {10.3390/computation9020020},
year = {2021},
date = {2021-01-01},
journal = {Computation},
volume = {9},
number = {2},
pages = {20},
publisher = {MDPI AG},
abstract = {In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.},
note = {MDPI Computation 2021 Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}