# CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors

arXiv: https://arxiv.org/abs/1904.03084 · HTML: https://mlsanity.com/paper/1904.03084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information](https://mlsanity.com/paper/2101.05499/) (2021)

## Abstract (arXiv)

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.