Generating Ethnographic Models from Communities’ Online Data

Tomek Strzalkowski, Anna Newheiser, Nathan Kemper, Ning Sa, Bharvee Acharya, Gregorios Katsios


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.
Anthology ID:
2020.figlang-1.23
Volume:
Proceedings of the Second Workshop on Figurative Language Processing
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | Fig-Lang | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
165–175
Language:
URL:
https://www.aclweb.org/anthology/2020.figlang-1.23
DOI:
10.18653/v1/2020.figlang-1.23
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.figlang-1.23.pdf
Software:
 2020.figlang-1.23.Software.zip
Dataset:
 2020.figlang-1.23.Dataset.pdf
Video:
 http://slideslive.com/38929711