  The explosive growth and popularity of Social Media has revolutionised the
way we communicate and collaborate. Unfortunately, this same ease of accessing
and sharing information has led to an explosion of misinformation and
propaganda. Given that stance detection can significantly aid in veracity
prediction, this work focuses on boosting automated stance detection, a task on
which pre-trained models have been extremely successful on, as on several other
tasks. This work shows that the task of stance detection can benefit from
feature based information, especially on certain under performing classes,
however, integrating such features into pre-trained models using ensembling is
challenging. We propose a novel architecture for integrating features with
pre-trained models that address these challenges and test our method on the
RumourEval 2019 dataset. This method achieves state-of-the-art results with an
F1-score of 63.94 on the test set.
