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)

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
https://doi.org/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/).

Download article (PDF)

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  - https://doi.org/10.2991/asum.k.210827.030
ID  - Hammoud2021
ER  -