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Studying Political Bias via Word Embeddings

Published: 20 April 2020 Publication History

Abstract

Machine Learning systems learn bias in addition to other patterns from input data on which they are trained. Bolukbasi et al. pioneered a method for quantifying gender bias learned from a corpus of text. Specifically, they compute a gender subspace into which words, represented as word vectors, can be placed and compared with one another. In this paper, we apply a similar methodology to a different type of bias, political bias. Unlike with gender bias, it is not obvious how to choose a set of definitional word pairs to compute a political bias subspace. We propose a methodology for doing so that could be used for modeling other types of bias as well. We collect and examine a 26 GB corpus of tweets from Republican and Democratic politicians in the United States (presidential candidates and members of Congress). With our definition of a political bias subspace, we observe several interesting and intuitive trends including that tweets from presidential candidates, both Republican and Democratic, show more political bias than tweets from other politicians of the same party. This work models political bias as a binary choice along one axis, as Bolukbasi et al. did for gender. However, most kinds of bias - political, racial and even gender bias itself - are much more complicated than two binary extremes along one axis. In this paper, we also discuss what might be required to model bias along multiple axes (e.g. liberal/conservative and authoritarian/libertarian for political bias) or as a range of points along a single axis (e.g. a gender spectrum).

References

[1]
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in neural information processing systems. 4349–4357.
[2]
Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. 77–91.
[3]
The Political Compass. 2017. The Political Compass - a brief intro. (2017). https://www.youtube.com/watch?v=5u3UCz0TM5Q
[4]
The Political Compass. 2017. Political Compass Test. (2017). https://www.politicalcompass.org/test
[5]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).
[6]
Brandon Griggs. 2014. Facebook goes beyond ’male’ and ’female’ with new gender options. (2014). https://www.cnn.com/2014/02/13/tech/social-media/facebook-gender-custom/index.html
[7]
Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, and Adam Tauman Kalai. 2019. What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes. CoRR abs/1904.05233(2019). arxiv:1904.05233http://arxiv.org/abs/1904.05233
[8]
Nathaniel Swinger, Maria De-Arteaga, Neil Thomas Heffernan IV, Mark D. M. Leiserson, and Adam Tauman Kalai. 2018. What are the biases in my word embedding?CoRR abs/1812.08769(2018). arxiv:1812.08769http://arxiv.org/abs/1812.08769

Cited By

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  • (2024)Ideological orientation and extremism detection in online social networking sites: A systematic reviewIntelligent Systems with Applications10.1016/j.iswa.2024.20045624(200456)Online publication date: Dec-2024
  • (2023)Detecting political biases of named entities and hashtags on TwitterEPJ Data Science10.1140/epjds/s13688-023-00386-612:1Online publication date: 8-Jun-2023
  • (2023)Biased or Debiased: Polarization-aware Embedding Learning from Social Media Knowledge Graph2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191992(1-8)Online publication date: 18-Jun-2023
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  1. Studying Political Bias via Word Embeddings
        Index terms have been assigned to the content through auto-classification.

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        cover image ACM Conferences
        WWW '20: Companion Proceedings of the Web Conference 2020
        April 2020
        854 pages
        ISBN:9781450370240
        DOI:10.1145/3366424
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        New York, NY, United States

        Publication History

        Published: 20 April 2020

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        Author Tags

        1. Twitter dataset
        2. natural language processing
        3. political bias

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        WWW '20: The Web Conference 2020
        April 20 - 24, 2020
        Taipei, Taiwan

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        Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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        Cited By

        View all
        • (2024)Ideological orientation and extremism detection in online social networking sites: A systematic reviewIntelligent Systems with Applications10.1016/j.iswa.2024.20045624(200456)Online publication date: Dec-2024
        • (2023)Detecting political biases of named entities and hashtags on TwitterEPJ Data Science10.1140/epjds/s13688-023-00386-612:1Online publication date: 8-Jun-2023
        • (2023)Biased or Debiased: Polarization-aware Embedding Learning from Social Media Knowledge Graph2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191992(1-8)Online publication date: 18-Jun-2023
        • (2022)Similarity analysis of federal reserve statements using document embeddings: the Great Recession vs. COVID-19SN Business & Economics10.1007/s43546-022-00248-92:7Online publication date: 18-Jun-2022
        • (2022)Algorithmic fairness datasets: the story so farData Mining and Knowledge Discovery10.1007/s10618-022-00854-z36:6(2074-2152)Online publication date: 17-Sep-2022

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