Abstract
Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.- Anthology ID:
- 2020.wnut-1.58
- Volume:
- Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 409–413
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.58
- DOI:
- 10.18653/v1/2020.wnut-1.58
- Cite (ACL):
- Sirigireddy Dhana Laxmi, Rohit Agarwal, and Aman Sinha. 2020. DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 409–413, Online. Association for Computational Linguistics.
- Cite (Informal):
- DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa (Dhana Laxmi et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/fix-volume-bibkeys/2020.wnut-1.58.pdf