Hidden Biases in Unreliable News Detection Datasets
Xiang Zhou, Heba Elfardy, Christos Christodoulopoulos, Thomas Butler, Mohit Bansal
Abstract
Automatic unreliable news detection is a research problem with great potential impact. Recently, several papers have shown promising results on large-scale news datasets with models that only use the article itself without resorting to any fact-checking mechanism or retrieving any supporting evidence. In this work, we take a closer look at these datasets. While they all provide valuable resources for future research, we observe a number of problems that may lead to results that do not generalize in more realistic settings. Specifically, we show that selection bias during data collection leads to undesired artifacts in the datasets. In addition, while most systems train and predict at the level of individual articles, overlapping article sources in the training and evaluation data can provide a strong confounding factor that models can exploit. In the presence of this confounding factor, the models can achieve good performance by directly memorizing the site-label mapping instead of modeling the real task of unreliable news detection. We observed a significant drop (>10%) in accuracy for all models tested in a clean split with no train/test source overlap. Using the observations and experimental results, we provide practical suggestions on how to create more reliable datasets for the unreliable news detection task. We suggest future dataset creation include a simple model as a difficulty/bias probe and future model development use a clean non-overlapping site and date split.- Anthology ID:
- 2021.eacl-main.211
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2482–2492
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.211
- DOI:
- 10.18653/v1/2021.eacl-main.211
- Award:
- Honorable Mention for Best Long Paper
- Cite (ACL):
- Xiang Zhou, Heba Elfardy, Christos Christodoulopoulos, Thomas Butler, and Mohit Bansal. 2021. Hidden Biases in Unreliable News Detection Datasets. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2482–2492, Online. Association for Computational Linguistics.
- Cite (Informal):
- Hidden Biases in Unreliable News Detection Datasets (Zhou et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2021.eacl-main.211.pdf
- Data
- NELA-GT-2018, NELA-GT-2019