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Evaluating the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions

Project description

Automatic content filtering in the fight against fake news

Natural language processing (NLP) and machine learning are considered countermeasures against misinformation and related challenges. For instance, NLP solutions can flag fake news, social media bots and the usage of propaganda techniques. But what about deliberate misinformation that is published in ways that can deceive any automatic filtering algorithm? The EU-funded ERINIA project will investigate methods for detecting adversarial examples and explore the robustness of text classifiers. Project findings will be disseminated to research communities in order to spark further discussion about the use of automatic content filtering in the fight against misinformation.


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.


Net EU contribution
€ 165 312,96
08002 Barcelona

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Este Cataluña Barcelona
Activity type
Higher or Secondary Education Establishments
Total cost
No data