George (Yiorgos) Stavropoulos

As an Application Architect at Cognity S.A., I’m based in the vibrant city of Athens, Greece, where I have the privilege of contributing to cutting-edge technology solutions.

My academic journey sparked a deep passion for technology, which I’ve channeled into addressing complex challenges in the tech industry.

Before moving to the industry, I received my diploma from the Department of Electrical and Computer Engineering of the University of Thessaly, Greece. In my undergraduate thesis, I worked with Assoc. Prof. Dimitrios Katsaros and DANA lab to research and evaluate various methods for detecting fake news using machine learning.

In my professional experience I have contributed to the digital transformation of various platforms gaining extensive expertise in secure transactions, microservices architecture and scalable invoicing/payment solutions.

Publications

WI’19
Which Machine Learning Paradigm for Fake News Detection?

Dimitrios Katsaros, George Stavropoulos, and Dimitrios. Papakostas

Proceedings of the IEEE/WIC/ACM Web Intelligence Conference, Oct 2019

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Fake news detection/classification is gradually becoming of paramount importance to out society in order to avoid the so-called reality vertigo, and protect in particular the less educated persons. Various machine learning techniques have been proposed to address this issue. This article presents a comprehensive performance evaluation of eight machine learning algorithms for fake news detection/classification.
@article{10.1145/3350546.3352552, title = {Which Machine Learning Paradigm for Fake News Detection?}, author = {Katsaros, Dimitrios and Stavropoulos, George and Papakostas, Dimitrios.}, journal = {Proceedings of the IEEE/WIC/ACM Web Intelligence Conference}, pages = {383--387}, year = {2019}, month = oct, publisher = ieee, doi = {10.1145/3350546.3352552}, url = {https://ieeexplore.ieee.org/document/8909583/authors}, google_scholar_id = {u5HHmVD_uO8C}, }
Springer’22
Evaluation of Machine Learning Methods for Fake News Detection

Dimitrios Papakostas, George Stavropoulos, and Dimitrios. Katsaros

Combating Fake News with Computational Intelligence Techniques, Oct 2022

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In a cyber-connected world, fake information appears to be more enticing or interesting to the audience because of their limited attention spans and the plethora of content choices. Taking this into account, fake news detection/classification is definitely becoming of paramount importance in order to avoid the so-called reality vertigo, preclude misinformation and protect actual reality. This chapter presents a comprehensive performance evaluation of eight machine learning algorithms who perform fake news detection/classification based on regression, support vector machines, neural networks, decision trees and Bayes theorem. In every case, our study reaffirms that performance is governed by the nature of data, nevertheless, it sheds light and draws safe generic conclusions with respect to the dimensionality that each algorithm should have, the kind of training that should be performed beforehand for each one of them, and finally the method for generating vector representations of textual information.
@article{10.1007/978-3-030-90087-8_8, title = {Evaluation of Machine Learning Methods for Fake News Detection}, author = {Papakostas, Dimitrios and Stavropoulos, George and Katsaros, Dimitrios.}, journal = {Combating Fake News with Computational Intelligence Techniques}, pages = {163--183}, year = {2022}, month = oct, publisher = springer, doi = {10.1007/978-3-030-90087-8_8}, url = {https://link.springer.com/book/10.1007/978-3-030-90087-8/}, google_scholar_id = {u-x6o8ySG0sC}, }