DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics

Ankit Srivastava, Georg Rehm, Julian Moreno Schneider


Abstract
We describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) participated in two subtasks: Subtask A (determining the stance of a message) and Subtask B (determining veracity of a message, closed variant). In both cases, our implementation consisted of a Multivariate Logistic Regression (Maximum Entropy) classifier coupled with hand-written patterns and rules (heuristics) applied in a post-process cascading fashion. We provide a detailed analysis of the system performance and report on variants of our systems that were not part of the official submission.
Anthology ID:
S17-2085
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
486–490
Language:
URL:
https://aclanthology.org/S17-2085/
DOI:
10.18653/v1/S17-2085
Bibkey:
Cite (ACL):
Ankit Srivastava, Georg Rehm, and Julian Moreno Schneider. 2017. DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 486–490, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics (Srivastava et al., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-2085.pdf