Descripción del proyecto
Filtrado automático de contenidos en la lucha contra las noticias falsas
El procesamiento del lenguaje natural (PLN) y el aprendizaje automático se consideran contramedidas para combatir la desinformación y los desafíos relacionados con ella. Por ejemplo, las soluciones de PLN pueden señalar noticias falsas, bots de redes sociales y el uso de técnicas de propaganda. ¿Pero qué pasa con la información deliberadamente errónea que se publica de tal manera que puede engañar a cualquier algoritmo de filtrado automático? El equipo del proyecto ERINIA, financiado con fondos europeos, investigará métodos para detectar ejemplos contradictorios y estudiará la solidez de los clasificadores de texto. Los hallazgos del proyecto se difundirán entre las comunidades de investigación para generar más debates sobre el uso del filtrado automático de contenidos en la lucha contra la desinformación.
Objetivo
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.
Ámbito científico
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-AG-UN - HORIZON Unit GrantCoordinador
08002 Barcelona
España