@inproceedings{strzalkowski-etal-2020-generating,
title = "Generating Ethnographic Models from Communities{'} Online Data",
author = "Strzalkowski, Tomek and
Newheiser, Anna and
Kemper, Nathan and
Sa, Ning and
Acharya, Bharvee and
Katsios, Gregorios",
editor = "Klebanov, Beata Beigman and
Shutova, Ekaterina and
Lichtenstein, Patricia and
Muresan, Smaranda and
Wee, Chee and
Feldman, Anna and
Ghosh, Debanjan",
booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.figlang-1.23",
doi = "10.18653/v1/2020.figlang-1.23",
pages = "165--175",
abstract = "In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities{'} models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community{'}s online language convey the bias in the community{'}s worldview.",
}
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<abstract>In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities’ models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community’s online language convey the bias in the community’s worldview.</abstract>
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%0 Conference Proceedings
%T Generating Ethnographic Models from Communities’ Online Data
%A Strzalkowski, Tomek
%A Newheiser, Anna
%A Kemper, Nathan
%A Sa, Ning
%A Acharya, Bharvee
%A Katsios, Gregorios
%Y Klebanov, Beata Beigman
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%Y Feldman, Anna
%Y Ghosh, Debanjan
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F strzalkowski-etal-2020-generating
%X In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities’ models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community’s online language convey the bias in the community’s worldview.
%R 10.18653/v1/2020.figlang-1.23
%U https://aclanthology.org/2020.figlang-1.23
%U https://doi.org/10.18653/v1/2020.figlang-1.23
%P 165-175
Markdown (Informal)
[Generating Ethnographic Models from Communities’ Online Data](https://aclanthology.org/2020.figlang-1.23) (Strzalkowski et al., Fig-Lang 2020)
ACL
- Tomek Strzalkowski, Anna Newheiser, Nathan Kemper, Ning Sa, Bharvee Acharya, and Gregorios Katsios. 2020. Generating Ethnographic Models from Communities’ Online Data. In Proceedings of the Second Workshop on Figurative Language Processing, pages 165–175, Online. Association for Computational Linguistics.