Identifying Misinformation Spreaders: A Graph-Based Semi-Supervised Learning Approach

Autor:	Ullah, Atta, Abbasi, Rabeeh Ayaz, Khattak, Akmal Saeed, Said, Anwar
Rok vydání:	2023
Předmět:	Computer Science - Social and Information Networks 91D30 I.2.1
Druh dokumentu:	Working Paper
Popis:	In this paper we proposed a Graph-Based conspiracy source detection method for the MediaEval task 2022 FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task. The goal of this study was to apply SOTA graph neural network methods to the problem of misinformation spreading in online social networks. We explore three different Graph Neural Network models: GCN, GraphSAGE and DGCNN. Experimental results demonstrate that DGCNN outperforms in terms of accuracy. Comment: Published in Multimedia Benchmark Workshop Proceedings 2022: https://2022.multimediaeval.com/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.03704 Zobrazit plný text záznamu View this record from Arxiv