  Recent research highlights the significant potential of ChatGPT for text
annotation in social science research. However, ChatGPT is a closed-source
product which has major drawbacks with regards to transparency,
reproducibility, cost, and data protection. Recent advances in open-source (OS)
large language models (LLMs) offer alternatives which remedy these challenges.
This means that it is important to evaluate the performance of OS LLMs relative
to ChatGPT and standard approaches to supervised machine learning
classification. We conduct a systematic comparative evaluation of the
performance of a range of OS LLM models alongside ChatGPT, using both zero- and
few-shot learning as well as generic and custom prompts, with results compared
to more traditional supervised classification models. Using a new dataset of
Tweets from US news media, and focusing on simple binary text annotation tasks
for standard social science concepts, we find significant variation in the
performance of ChatGPT and OS models across the tasks, and that supervised
classifiers consistently outperform both. Given the unreliable performance of
ChatGPT and the significant challenges it poses to Open Science we advise
against using ChatGPT for substantive text annotation tasks in social science
research.
