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ERINIA

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Evaluating the Robustness of Non-Credible Text Identification by Anticipating Ad...

Evaluating the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions 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 counter... ver más

31/10/2024

UPF

165K€

Presupuesto del proyecto: 165K€

Líder del proyecto

UNIVERSITAT POMPEU FABRA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 321

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-10-31

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

I+D

Cerrada hace 3 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UPF

165.31K€ | Lider

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Duración del proyecto: 27 meses Fecha Inicio: 2022-07-08
Fecha Fin: 2024-10-31

Líder del proyecto

UNIVERSITAT POMPEU FABRA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 321

Presupuesto del proyecto 165K€

Descripción del proyecto 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.

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