Abstract
Automatically identifying rumours in social media and assessing their veracity is an important task with downstream applications in journalism. A significant challenge is how to keep rumour analysis tools up-to-date as new information becomes available for particular rumours that spread in a social network. This paper presents a novel open-source web-based rumour analysis tool that can continuous learn from journalists. The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface. The feedback allows the system to improve an underlying state-of-the-art neural network-based rumour classification model. The system can be easily integrated as a service into existing tools and platforms used by journalists using a REST API.- Anthology ID:
- D19-3020
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Sebastian Padó, Ruihong Huang
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 115–120
- Language:
- URL:
- https://aclanthology.org/D19-3020
- DOI:
- 10.18653/v1/D19-3020
- Cite (ACL):
- Twin Karmakharm, Nikolaos Aletras, and Kalina Bontcheva. 2019. Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 115–120, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis (Karmakharm et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/D19-3020.pdf