  Social media datasets are essential for research on disinformation, influence
operations, social sensing, hate speech detection, cyberbullying, and other
significant topics. However, access to these datasets is often restricted due
to costs and platform regulations. As such, acquiring datasets that span
multiple platforms which are crucial for a comprehensive understanding of the
digital ecosystem is particularly challenging. This paper explores the
potential of large language models to create lexically and semantically
relevant social media datasets across multiple platforms, aiming to match the
quality of real datasets. We employ ChatGPT to generate synthetic data from two
real datasets, each consisting of posts from three different social media
platforms. We assess the lexical and semantic properties of the synthetic data
and compare them with those of the real data. Our empirical findings suggest
that using large language models to generate synthetic multi-platform social
media data is promising. However, further enhancements are necessary to improve
the fidelity of the outputs.
