Abstract
While fake news detection received quite a bit of attention in recent years, hyperpartisan news detection is still an underresearched topic. This paper presents our work towards building a classification system for hyperpartisan news detection in the context of the SemEval2019 shared task 4. We experiment with two different approaches - a more stylistic one, and a more content related one - achieving average results.- Anthology ID:
- S19-2168
- 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:
- 976–980
- Language:
- URL:
- https://aclanthology.org/S19-2168
- DOI:
- 10.18653/v1/S19-2168
- Cite (ACL):
- Jürgen Knauth. 2019. Orwellian-times at SemEval-2019 Task 4: A Stylistic and Content-based Classifier. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 976–980, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Orwellian-times at SemEval-2019 Task 4: A Stylistic and Content-based Classifier (Knauth, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/S19-2168.pdf