REM: Efficient Semi-Automated Real-Time Moderation of Online Forums

Jakob Smedegaard Andersen, Olaf Zukunft, Walid Maalej


Abstract
This paper presents REM, a novel tool for the semi-automated real-time moderation of large scale online forums. The growing demand for online participation and the increasing number of user comments raise challenges in filtering out harmful and undesirable content from public debates in online forums. Since a manual moderation does not scale well and pure automated approaches often lack the required level of accuracy, we suggest a semi-automated moderation approach. Our approach maximizes the efficiency of manual efforts by targeting only those comments for which human intervention is needed, e.g. due to high classification uncertainty. Our tool offers a rich visual interactive environment enabling the exploration of online debates. We conduct a preliminary evaluation experiment to demonstrate the suitability of our approach and publicly release the source code of REM.
Anthology ID:
2021.acl-demo.17
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Month:
August
Year:
2021
Address:
Online
Editors:
Heng Ji, Jong C. Park, Rui Xia
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
142–149
Language:
URL:
https://aclanthology.org/2021.acl-demo.17
DOI:
10.18653/v1/2021.acl-demo.17
Bibkey:
Cite (ACL):
Jakob Smedegaard Andersen, Olaf Zukunft, and Walid Maalej. 2021. REM: Efficient Semi-Automated Real-Time Moderation of Online Forums. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 142–149, Online. Association for Computational Linguistics.
Cite (Informal):
REM: Efficient Semi-Automated Real-Time Moderation of Online Forums (Andersen et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.17.pdf
Video:
 https://aclanthology.org/2021.acl-demo.17.mp4