Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics
Nazanin Afsarmanesh, Jussi Karlgren, Peter Sumbler, Nina Viereckel
Abstract
This report describes the starting point for a simple rule based hypothesis testing excercise on identifying hyperpartisan news items carried out by the Harry Friberg team from Gavagai. We used manually crafted metatopics, topics which often appear in hyperpartisan texts as rant conduits, together with tonality analysis to identify general characteristics of hyperpartisan news items. While the precision of the resulting effort is less than stellar— our contribution ranked 37th of the 42 successfully submitted experiments with overly high recall (95%) and low precision (54%)—we believe we have a model which allows us to continue exploring the underlying features of what the subgenre of hyperpartisan news items is characterised by.- Anthology ID:
- S19-2174
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1004–1006
- Language:
- URL:
- https://aclanthology.org/S19-2174
- DOI:
- 10.18653/v1/S19-2174
- Cite (ACL):
- Nazanin Afsarmanesh, Jussi Karlgren, Peter Sumbler, and Nina Viereckel. 2019. Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1004–1006, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics (Afsarmanesh et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S19-2174.pdf