# Observing the Southern US Culture of Honor Using Large-Scale Social Media Analysis

arXiv: https://arxiv.org/abs/2410.13887 · HTML: https://mlsanity.com/paper/2410.13887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community](https://mlsanity.com/paper/1810.13181/) (2018)

## Built on by (most cited first, 1 of 1)

- [An LLM-Integrated Framework for Completion, Management, and Tracing of STPA](https://mlsanity.com/paper/2503.12043/) (2025)

## Abstract (arXiv)

A textitculture 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 textitculture of honor is more prevalent, are more likely to display a trait predicted by their belonging to a textitculture 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.