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.

FbMultiLingMisinfo: Challenging Large-Scale Multilingual Benchmark for Misinformation Detection / Barnabò, Giorgio; Siciliano, Federico; Carlos, Castillo; Leonardi, Stefano; Nakov, Preslav; Da San Martino, Giovanni; Silvestri, Fabrizio. - (2022), pp. 1-8. (Intervento presentato al convegno 2022 International Joint Conference on Neural Networks (IJCNN) tenutosi a Padova, Italia).

FbMultiLingMisinfo: Challenging Large-Scale Multilingual Benchmark for Misinformation Detection

Federico Siciliano;Carlos Castillo;Stefano Leonardi;Fabrizio Silvestri
2022

Abstract

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)
Misinformation Detection; Social Network; Graph Neural Networks
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
FbMultiLingMisinfo: Challenging Large-Scale Multilingual Benchmark for Misinformation Detection / Barnabò, Giorgio; Siciliano, Federico; Carlos, Castillo; Leonardi, Stefano; Nakov, Preslav; Da San Martino, Giovanni; Silvestri, Fabrizio. - (2022), pp. 1-8. (Intervento presentato al convegno 2022 International Joint Conference on Neural Networks (IJCNN) tenutosi a Padova, Italia).
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1669826
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 0
social impact