  This paper describes our submission to SemEval-2019 Task 7: RumourEval:
Determining Rumor Veracity and Support for Rumors. We participated in both
subtasks. The goal of subtask A is to classify the type of interaction between
a rumorous social media post and a reply post as support, query, deny, or
comment. The goal of subtask B is to predict the veracity of a given rumor. For
subtask A, we implement a CNN-based neural architecture using ELMo embeddings
of post text combined with auxiliary features and achieve a F1-score of 44.6%.
For subtask B, we employ a MLP neural network leveraging our estimates for
subtask A and achieve a F1-score of 30.1% (second place in the competition). We
provide results and analysis of our system performance and present ablation
experiments.
