Hotter and Colder: A New Approach to Annotating Sentiment, Emotions, and Bias in Icelandic Blog Comments

TL;DR

Hotter and Colder introduces a comprehensive Icelandic online comment dataset annotated for sentiment, emotions, and bias, combining automated and manual labeling to support research in content moderation and harmful behavior detection.

Contribution

The paper presents a new large-scale annotated dataset for Icelandic online comments, integrating GPT-4o mini automation with manual review to improve annotation quality.

Findings

Created 12,232 manually verified annotations

Annotated 800,000 comments across 25 tasks

Enhanced dataset for content moderation research

Abstract

This paper presents Hotter and Colder, a dataset designed to analyze various types of online behavior in Icelandic blog comments. Building on previous work, we used GPT-4o mini to annotate approximately 800,000 comments for 25 tasks, including sentiment analysis, emotion detection, hate speech, and group generalizations. Each comment was automatically labeled on a 5-point Likert scale. In a second annotation stage, comments with high or low probabilities of containing each examined behavior were subjected to manual revision. By leveraging crowdworkers to refine these automatically labeled comments, we ensure the quality and accuracy of our dataset resulting in 12,232 uniquely annotated comments and 19,301 annotations. Hotter and Colder provides an essential resource for advancing research in content moderation and automatically detectiong harmful online behaviors in Icelandic.