# Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups

## TL;DR

This paper investigates the dynamic linguistic relations in conversations with explicit winners and losers, using Supreme Court data to understand how opinions and arguments evolve through cooperation and competition.

## Contribution

It introduces a method to uncover self-organized linguistic relations in conversations with explicit outcome labels, enhancing understanding of opinion dynamics in decision-making contexts.

## Key findings

- Linguistic relations evolve differently in winning and losing sides.
- The approach reveals complex dynamic events in verbal communications.
- Insights can improve opinion mining and decision-making models.

## Abstract

Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. The relations bond ideas, arguments, thoughts, and feelings, re-shape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speaker's reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations hidden in a conversation with explicitly stated winners and losers. We examine open access data of the United States Supreme Court. Our understanding is crucial in big data research to guide how transition states in opinion mining and decision-making should be modeled and how this required knowledge to guide the model should be pinpointed, by filtering large amount of data.