Abstract
Social media plays a crucial role as the main resource news for information seekers online. However, the unmoderated feature of social media platforms lead to the emergence and spread of untrustworthy contents which harm individuals or even societies. Most of the current automated approaches for automatically determining the veracity of a rumor are not generalizable for novel emerging topics. This paper describes our hybrid system comprising rules and a machine learning model which makes use of replied tweets to identify the veracity of the source tweet. The proposed system in this paper achieved 0.435 F-Macro in stance classification, and 0.262 F-macro and 0.801 RMSE in rumor verification tasks in Task7 of SemEval 2019.- Anthology ID:
- S19-2195
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1115–1119
- Language:
- URL:
- https://aclanthology.org/S19-2195
- DOI:
- 10.18653/v1/S19-2195
- Cite (ACL):
- Sardar Hamidian and Mona Diab. 2019. GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1115–1119, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media (Hamidian & Diab, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S19-2195.pdf