According to recent research, geometric deep learning allows to reach unprecedented accuracy for online misinformation detection. By fully leveraging the news social context, URL propagation paths in social networks are first represented as graphs and then classified using Graph Neural Network (GNN) models. Despite these remarkable efforts, researchers are still hampered by the scarcity of high-quality benchmark datasets, and as a result, the efficacy of state-of-the-art approaches could be overestimated. So far, in order to obtain a decent number of third-party fact-checked URLs, researchers have either sampled news from notoriously reliable and unreliable sources using distant supervision, or they have gathered pre-labeled URLs from third-party fact-checking websites. In the former case, resulting datasets can be quite large, but also noisy and biased since pieces of news are labeled as true or false.
2022, 2022 International Joint Conference on Neural Networks (IJCNN), Pages 1-8
FbMultiLingMisinfo: Challenging Large-Scale Multilingual Benchmark for Misinformation Detection (04b Atto di convegno in volume)
Barnabò Giorgio, Siciliano Federico, Carlos Castillo, Leonardi Stefano, Nakov Preslav, Da San Martino Giovanni, Silvestri Fabrizio