CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors
This work addresses rumor analysis for social media platforms, but it is incremental as it applies existing methods like CNNs and ELMo to a specific dataset.
The paper tackled the problem of classifying interactions and predicting veracity of rumors on social media, achieving an F1-score of 44.6% for interaction classification and 30.1% for veracity prediction, placing second in the competition.
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.