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.

翻译：社交媒体数据集对于虚假信息、影响力操作、社会感知、仇恨言论检测、网络欺凌及其他重要议题的研究至关重要。然而，由于成本和平台监管限制，获取这些数据集往往受限。因此，获取跨多平台的数据集——这对全面理解数字生态系统尤为关键——尤其具有挑战性。本文探讨了利用大语言模型生成跨多平台、在词汇和语义层面相关的社交媒体数据集的潜力，旨在匹配真实数据集的质量。我们使用ChatGPT从两个真实数据集生成合成数据，每个数据集包含来自三个不同社交媒体平台的帖子。我们评估了合成数据的词汇和语义特性，并将其与真实数据进行比较。实证结果表明，利用大语言模型生成合成多平台社交媒体数据具有前景，但需进一步改进以提升输出结果的保真度。