Adversarial Training for Fake News Classification

Abstract

News is a source of information to know about progress in the various areas of life all across the globe. However, the volume of this information is high, and getting benefits from the available information becomes difficult. Moreover, the frequency of fake news is increasing significantly and used to fulfill a particular agenda. This led to research on the classification of news to prevent the spread of disinformation. In this work, we use Adversarial Training as a means of regularization for fake news classification. We train two transformed-based encoder models using adversarial examples that help the model learn noise invariant representations. We generate these examples by perturbing the model’s word embedding matrix, and then we fine-tune the model on clean and adversarial examples simultaneously. We train and evaluate the models on the Buzzfeed Political News and Random Political News datasets. Results show consistent improvements over the baseline models when we train models using adversarial examples. Experiments show that Adversarial Training improves the performance by 1.25% over the BERT baseline, 2.05% over the Longformer baseline for the Random Political News dataset, 1.25% over the BERT baseline and 0.9% over the Longformer baseline for Buzzfeed Political News dataset in terms of F1-score.