Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/25909| Title: | Exploring the Generalisability of Fake News Detection Models |
| Authors: | Hoy, N Koulouri, T |
| Keywords: | fake news detection;natural language processing;machine learning;generalisability |
| Issue Date: | 26-Jan-2023 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Citation: | Hoy, N. and Koulouri, T. (2023) 'Exploring the Generalisability of Fake News Detection Models', 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, pp. 5731 - 5740. doi: 10.1109/bigdata55660.2022.10020583. |
| URI: | https://bura.brunel.ac.uk/handle/2438/25909 |
| DOI: | https://doi.org/10.1109/bigdata55660.2022.10020583 |
| ISBN: | 978-1-6654-8045-1 (ebk) 978-1-6654-8046-8 (PoD) |
| Other Identifiers: | ORCID iD: Theodora Koulouri https://orcid.org/0000-0001-9588-9647 |
| Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | 226.97 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.