most citedChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning

161 citations · 198 across the 3 of their papers we have counts for

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cs.CL2025

Computational Turing Test Reveals Systematic Differences Between Human and AI Language

Nicolò Pagan, Petter Törnberg, Christopher A. Bail +2

Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior, based on the assumption that they can generate realistic, human-like text. Yet…

cs.CL2025

Emergent LLM behaviors are observationally equivalent to data leakage

Christopher Barrie, Petter Törnberg

Ashery et al. recently argue that large language models (LLMs), when paired to play a classic "naming game," spontaneously develop linguistic conventions reminiscent of human socia…

cs.CL202423 cited

Best Practices for Text Annotation with Large Language Models

Petter Törnberg

Large Language Models (LLMs) have ushered in a new era of text annotation, as their ease-of-use, high accuracy, and relatively low costs have meant that their use has exploded in r…

cs.CL202319 cited

How to use LLMs for Text Analysis

Petter Törnberg

This guide introduces Large Language Models (LLM) as a highly versatile text analysis method within the social sciences. As LLMs are easy-to-use, cheap, fast, and applicable on a b…

cs.CL2023161 cited

ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning

Petter Törnberg

This paper assesses the accuracy, reliability and bias of the Large Language Model (LLM) ChatGPT-4 on the text analysis task of classifying the political affiliation of a Twitter p…