@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://preview.aclanthology.org/jlcl-multiple-ingestion/2020.coling-main.428/",
    doi = "10.18653/v1/2020.coling-main.428",
    pages = "4873--4885",
    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."
}