TY - GEN
T1 - Observing the Southern US Culture of Honor Using Large-Scale Social Media Analysis
AU - Kim, Juho
AU - Guerzhoy, Michael
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - A culture of honor refers to a social system where individuals’ status, reputation, and esteem play a central role in governing interpersonal relations. Past works have associated this concept with the United States (US) South and related with it various traits such as higher sensitivity to insult, a higher value on reputation, and a tendency to react violently to insults. In this paper, we hypothesize and confirm that internet users from the US South, where a culture of honor is more prevalent, are more likely to display a trait predicted by their belonging to a culture of honor. Specifically, we test the hypothesis that US Southerners are more likely to retaliate to personal attacks by personally attacking back. We leverage OpenAI’s GPT-3.5 API to both geolocate internet users and to automatically detect whether users are insulting each other. We validate the use of GPT-3.5 by measuring its performance on manually-labeled subsets of the data. Our work demonstrates the potential of formulating a hypothesis based on a conceptual framework, operationalizing it in a way that is amenable to large-scale LLM-aided analysis, manually validating the use of the LLM, and drawing a conclusion.
AB - A culture of honor refers to a social system where individuals’ status, reputation, and esteem play a central role in governing interpersonal relations. Past works have associated this concept with the United States (US) South and related with it various traits such as higher sensitivity to insult, a higher value on reputation, and a tendency to react violently to insults. In this paper, we hypothesize and confirm that internet users from the US South, where a culture of honor is more prevalent, are more likely to display a trait predicted by their belonging to a culture of honor. Specifically, we test the hypothesis that US Southerners are more likely to retaliate to personal attacks by personally attacking back. We leverage OpenAI’s GPT-3.5 API to both geolocate internet users and to automatically detect whether users are insulting each other. We validate the use of GPT-3.5 by measuring its performance on manually-labeled subsets of the data. Our work demonstrates the potential of formulating a hypothesis based on a conceptual framework, operationalizing it in a way that is amenable to large-scale LLM-aided analysis, manually validating the use of the LLM, and drawing a conclusion.
UR - http://www.scopus.com/inward/record.url?scp=85216926371&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85216926371&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85216926371
T3 - SICon 2024 - 2nd Workshop on Social Influence in Conversations, Proceedings of the Workshop
SP - 1
EP - 8
BT - SICon 2024 - 2nd Workshop on Social Influence in Conversations, Proceedings of the Workshop
A2 - Hale, James
A2 - Chawla, Kushal
A2 - Garg, Muskan
PB - Association for Computational Linguistics (ACL)
T2 - 2nd Workshop on Social Influence in Conversations, SICon 2024
Y2 - 16 November 2024
ER -