Exploring a Federated Learning Approach to Enhance Authorship Attribution of Misleading Information from Heterogeneous Sources
Published in Proceedings of the International Joint Conference on Neural Networks, 2021
Recommended citation: Fiammetta Marulli, Antonio Balzanella, Lelio Campanile, Mauro Iacono, Michele Mastroianni, "Exploring a Federated Learning Approach to Enhance Authorship Attribution of Misleading Information from Heterogeneous Sources." Proceedings of the International Joint Conference on Neural Networks, 2021. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116453728&doi=10.1109%2fIJCNN52387.2021.9534377&partnerID=40&md5=1a66f680877de40ba6525a359903672e
Cited by: 7
Abstract: Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work. © 2021 IEEE.
Author Keywords: Authorship Attribution; Cooperative Computing; Federated Learning; Natural Language Processing; Text classification
Bibtex citation:
@CONFERENCE{Marulli2021,
author = "Marulli, Fiammetta and Balzanella, Antonio and Campanile, Lelio and Iacono, Mauro and Mastroianni, Michele",
title = "Exploring a Federated Learning Approach to Enhance Authorship Attribution of Misleading Information from Heterogeneous Sources",
year = "2021",
journal = "Proceedings of the International Joint Conference on Neural Networks",
volume = "2021-July",
doi = "10.1109/IJCNN52387.2021.9534377",
type = "Conference paper"
}