  Many NLP applications require manual data annotations for a variety of tasks,
notably to train classifiers or evaluate the performance of unsupervised
models. Depending on the size and degree of complexity, the tasks may be
conducted by crowd-workers on platforms such as MTurk as well as trained
annotators, such as research assistants. Using a sample of 2,382 tweets, we
demonstrate that ChatGPT outperforms crowd-workers for several annotation
tasks, including relevance, stance, topics, and frames detection. Specifically,
the zero-shot accuracy of ChatGPT exceeds that of crowd-workers for four out of
five tasks, while ChatGPT's intercoder agreement exceeds that of both
crowd-workers and trained annotators for all tasks. Moreover, the
per-annotation cost of ChatGPT is less than $0.003 -- about twenty times
cheaper than MTurk. These results show the potential of large language models
to drastically increase the efficiency of text classification.
