OneLove beyond the field -- A few-shot pipeline for topic and sentiment analysis during the FIFA World Cup in Qatar

TL;DR

This paper presents a few-shot learning pipeline using large language models to analyze Twitter discussions on the OneLove armband during the FIFA World Cup, capturing topic shifts and sentiment changes in real-time.

Contribution

It introduces a novel few-shot in-context learning approach with LLMs for dynamic topic and sentiment analysis during unfolding events, validated against human annotations.

Findings

Initial focus on armband impact, LGBT rights, and politics.

Post-ban shift towards general sports politics.

Sentiment became more neutral after the controversy.

Abstract

The FIFA World Cup in Qatar was discussed extensively in the news and on social media. Due to news reports with allegations of human rights violations, there were calls to boycott it. Wearing a OneLove armband was part of a planned protest activity. Controversy around the armband arose when FIFA threatened to sanction captains who wear it. To understand what topics Twitter users Tweeted about and what the opinion of German Twitter users was towards the OneLove armband, we performed an analysis of German Tweets published during the World Cup using in-context learning with LLMs. We validated the labels on human annotations. We found that Twitter users initially discussed the armband's impact, LGBT rights, and politics; after the ban, the conversation shifted towards politics in sports in general, accompanied by a subtle shift in sentiment towards neutrality. Our evaluation serves as a framework for future research to explore the impact of sports activism and evolving public sentiment. This is especially useful in settings where labeling datasets for specific opinions is unfeasible, such as when events are unfolding.