In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. Our system relies on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic in the sense that they promote a particular political cause or viewpoint. We trained a logistic regression model with features ranging from simple bag-of-words to vocabulary richness and text readability features. Our system achieved 72.9% accuracy on the test data that is annotated manually and 60.8% on the test data that is annotated with distant supervision. Additional experiments showed that significant performance improvements can be achieved with better feature pre-processing.
@article{arxiv.1904.03513,
title = {Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection},
author = {Abdelrhman Saleh and Ramy Baly and Alberto Barrón-Cedeño and Giovanni Da San Martino and Mitra Mohtarami and Preslav Nakov and James Glass},
journal= {arXiv preprint arXiv:1904.03513},
year = {2019}
}