A Sentiment-Based Author Verification Model Against Social Media Fraud
Authors
Khodor Hammoud, Salima Benbernou, Mourad Ouziri
Corresponding Author
Khodor Hammoud
Available Online 30 August 2021.
- DOI
- 10.2991/asum.k.210827.030How to use a DOI?
- Keywords
- authorship verification, machine learning, sentiment analysis, short text, keyword search
- Abstract
The widespread and capability of Iot devices have made them a primary enabler for online fraud and fake authorship on social media. We present a novel approach, which uses sentiment analysis, to solve the problem of author verification in short text. We perform experimentation with our model on tweets, and show that it yields promising results.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Khodor Hammoud AU - Salima Benbernou AU - Mourad Ouziri PY - 2021 DA - 2021/08/30 TI - A Sentiment-Based Author Verification Model Against Social Media Fraud BT - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP) PB - Atlantis Press SP - 219 EP - 226 SN - 2589-6644 UR - https://doi.org/10.2991/asum.k.210827.030 DO - 10.2991/asum.k.210827.030 ID - Hammoud2021 ER -