Leveraging GPT for the Generation of Multi-Platform Social Media Datasets for Research
Authors: 
Henry Tari, M. Danial Khan, Justus Rutten, Darian Othman, Thales Bertaglia, Rishabh Kaushal, 
Adriana IamnitchiAuthors Info & Claims
Pages 337 - 343
Abstract
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 a real dataset 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.
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September 2024
415 pages
ISBN:9798400705953
DOI:10.1145/3648188
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Published: 10 September 2024
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