Please score 1-5 for the following abstract. This abstract is from an academic paper.

The release of ChatGPT has uncovered a range of possibilities
whereby large language models (LLMs) can substitute human intel-
ligence. In this paper, we seek to understand whether ChatGPT has
the potential to reproduce human-generated label annotations in
social computing tasks. Such an achievement could significantly re-
duce the cost and complexity of social computing research. As such,
we use ChatGPT to re-label five seminal datasets covering stance
detection (2x), sentiment analysis, hate speech, and bot detection.
Our results highlight that ChatGPT does have the potential to han-
dle these data annotation tasks, although a number of challenges
remain. ChatGPT obtains an average accuracy 0.609. Performance is
highest for the sentiment analysis dataset, with ChatGPT correctly
annotating 64.9% of tweets. Yet, we show that performance varies
substantially across individual labels. We believe this work can
open up new lines of analysis and act as a basis for future research
into the exploitation of ChatGPT for human annotation tasks