COLING 2020main10 citations
Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models
Meiqi Guo, Rebecca Hwa, Yu-Ru Lin, Wen-Ting Chung
Abstract
We investigate the impact of political ideology biases in training data. Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its effect on the overall retrieval accuracy. Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input, which may lead to a deterioration of retrieval accuracy, and the importance of controlling for these biases. Finally, as a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance.
BibTeX
@inproceedings{guo-etal-2020-inflating,
title = "Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models",
author = "Guo, Meiqi and
Hwa, Rebecca and
Lin, Yu-Ru and
Chung, Wen-Ting",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.428/",
doi = "10.18653/v1/2020.coling-main.428",
pages = "4873--4885"
}