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The explosive growth and popularity of Social Media has revolutionised theway we communicate and collaborate. Unfortunately, this same ease of accessingand sharing information has led to an explosion of misinformation andpropaganda. Given that stance detection can significantly aid in veracityprediction, this work focuses on boosting automated stance detection, a task onwhich pre-trained models have been extremely successful on, as on several othertasks. This work shows that the task of stance detection can benefit fromfeature based information, especially on certain under performing classes,however, integrating such features into pre-trained models using ensembling ischallenging. We propose a novel architecture for integrating features withpre-trained models that address these challenges and test our method on theRumourEval 2019 dataset. This method achieves state-of-the-art results with anF1-score of 63.94 on the test set.

Incorporating Count-Based Features into Pre-Trained Models for Improved Stance Detection