Description du projet
Le filtrage automatique du contenu dans la lutte contre les fausses informations
Le traitement du langage naturel (TLN) et l’apprentissage automatique sont considérés comme des contremesures déployées pour lutter contre la mésinformation et relever les défis qui en découlent. Par exemple, les solutions TLN permettent de signaler de fausses informations, des bots de médias sociaux et l’utilisation de techniques de propagande. Mais qu’en est-il de la mésinformation délibérée publiée de manière à tromper les algorithmes de filtrage automatique? Le projet ERINIA, financé par l’UE, examinera les méthodes utilisées pour détecter des exemples contradictoires ainsi que la fiabilité des classificateurs de textes. Les conclusions du projet seront communiquées aux communautés de la recherche afin d’alimenter la discussion sur l’utilisation du filtrage automatique du contenu dans la lutte contre la mésinformation.
Objectif
As challenges posed by misinformation become apparent in the modern digital society, state-of-the-art methods of Artificial Intelligence, especially Natural Language Processing (NLP) and Machine Learning, are considered as countermeasures. Indeed, previous research has shown that NLP solutions can detect phenomena such as fake news, social media bots or usage of propaganda techniques. However, little attention has been given to the robustness of these approaches, which is especially important in the case of deliberate misinformation, whose authors would likely attempt to deceive any automatic filtering algorithm to achieve their goals.
The goal of the ERINIA project is to explore the robustness of text classifiers in this application area by investigating methods for detecting adversarial examples. Such methods aim to perform small perturbations to a given text piece, so that its meaning is preserved, but the output of the investigated classifier is reversed. To that end, previously unexplored directions will be pursued, including training reinforcement learning solutions and leveraging research on simplification and style transfer. Finally, the developed tools will be used to check the robustness of the current state-of-the-art misinformation detection solutions.
The project includes a range of training activities for the researcher and a plan for dissemination of the obtained results to various research communities. It also takes into account the society at large, as the project outcomes can inform further discussion on whether automatic content filtering is a viable solution to the misinformation problem.
Champ scientifique
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-AG-UN - HORIZON Unit GrantCoordinateur
08002 Barcelona
Espagne