DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Table 2: Model parameters used for each dataset (SN: Snopes, PF: PolitiFact, NT: NewsTrust, SE: SemEval).
['[BOLD] Parameter', '[BOLD] SN', '[BOLD] PF', '[BOLD] NT', '[BOLD] SE']
When using the Snopes, PolitiFact and NewsTrust datasets, we reserve 10% of the data as validation data for parameter tuning. We report 10-fold cross validation results on the remaining 90% of the data; the model is trained on 9-folds and the remaining fold is used as test data. When using the SemEval dataset, we use the data splits provided by the task’s organizers. The objective for Snopes, PolitiFact and SemEval experiments is binary (credibility) classification, while for NewsTrust the objective is to predict the credibility score of the input claim on a scale of 1 to 5 (i.e., credibility regression). We represent terms using pre-trained GloVe Wikipedia 6B word embeddings Pennington et al. Since our training datasets are not very large, we do not tune the word embeddings during training. We use Keras with a Tensorflow backend to implement our system. All the models are trained using Adam optimizer Kingma and Ba (learning rate: 0.002) with categorical cross-entropy loss for classification and mean squared error loss for regression task. We use L2-regularizers with the fully connected layers as well as dropout. For all the datasets, the model is trained using each claim-article pair as a separate training instance.