AnnotateThis: Analyzing a human-LLM system for annotating social media data with the concept of climate change mitigation pessimism

2026-06-08

AI Summary

This study addresses the limitations of large language models (LLMs) in annotating complex social science constructs—such as climate mitigation pessimism—where autonomous labeling often yields suboptimal quality. To overcome this, the authors propose AnnotateThis, a human-centered interactive annotation system that introduces an innovative “LLM grounding” paradigm, deeply integrating expert knowledge into the LLM annotation pipeline. The system enables iterative co-evolution of conceptual definitions and model refinement through human–AI collaboration, interactive visualizations, and dynamic prompt optimization, functioning effectively both with and without ground-truth labels. Empirical evaluation demonstrates that, in labeled settings, AnnotateThis achieves a 0.15 improvement in F-Measure and a 0.23 gain in accuracy, significantly outperforming existing fully automated approaches.

Abstract

Large language models (LLMs) are increasingly being integrated into research workflows. However, LLMs have been shown to struggle with difficult and nuanced concepts such as those found in computational social science (CSS) research. Within the CSS community, there has been a call for new systems to be developed which center humans in LLM-supported scientific workflows. We develop AnnotateThis, a human-centered system for inspecting and improving LLM annotations, a process we refer to as LLM grounding for a target concept. AnnotateThis is developed with both computational and social scientists to reflect existing workflows for data annotation. It includes a range of information features for users to interrogate the quality and reliability of LLM annotations. We evaluate our system in two settings. In the first, we assume a researcher may not have access to ground truth data and that users of AnnotateThis have limited prior knowledge of the concept they would like an LLM to annotate. That is, they may be conducting concept specification and LLM grounding simultaneously. In the second setting, we assume access to ground truth labels and that the concept is specified for a given annotation task; here, the task of LLM grounding is more straightforward. We find that in both settings users can improve the quality of LLM annotations with AnnotateThis and that their final annotations far surpass those created without human intervention. For example, when we evaluate with ground truth labels, we see an absolute improvement of 0.15 in F-Measure and 0.23 in accuracy over a fully automated state-of-the-art method for prompt refinement.

Problem

• large language models
• computational social science
• data annotation
• climate change mitigation pessimism
• human-AI collaboration

Innovation

• human-centered AI
• LLM grounding
• annotation system
• computational social science
• concept specification