Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection
Viresh Gupta, Baani Leen Kaur Jolly, Ramneek Kaur, Tanmoy Chakraborty
Abstract
In this paper, we present a news bias prediction system, which we developed as part of a SemEval 2019 task. We developed an XGBoost based system which uses character and word level n-gram features represented using TF-IDF, count vector based correlation matrix, and predicts if an input news article is a hyperpartisan news article. Our model was able to achieve a precision of 68.3% on the test set provided by the contest organizers. We also run our model on the BuzzFeed corpus and find XGBoost with simple character level N-Gram embeddings to be performing well with an accuracy of around 96%.- Anthology ID:
- S19-2159
- 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:
- 934–938
- Language:
- URL:
- https://aclanthology.org/S19-2159
- DOI:
- 10.18653/v1/S19-2159
- Cite (ACL):
- Viresh Gupta, Baani Leen Kaur Jolly, Ramneek Kaur, and Tanmoy Chakraborty. 2019. Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 934–938, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection (Gupta et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S19-2159.pdf