The Keyword Explorer Suite: A Toolkit for Understanding Online Populations

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2301.05198.pdf (311.91 KB)

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https://dl.acm.org/doi/abs/10.1145/3581754.3584122

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https://doi.org/10.1145/3581754.3584122
 http://hdl.handle.net/11603/31711

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Author/Creator ORCID

https://orcid.org/0000-0001-6164-6620
 https://orcid.org/0000-0002-5989-8543
 https://orcid.org/0000-0003-0935-4182

Date

2023-03-27

Type of Work

conference papers and proceedings
preprints
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Citation of Original Publication

Philip G Feldman, Shimei Pan, and James Foulds. 2023. The Keyword Explorer Suite: A Toolkit for Understanding Online Populations. In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI '23 Companion). Association for Computing Machinery, New York, NY, USA, 21–24. https://doi.org/10.1145/3581754.3584122

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Abstract

We have developed a set of Python applications that use large language models to identify and analyze data from social media platforms relevant to a population of interest. Our pipeline begins with using OpenAI’s GPT-3 to generate potential keywords for identifying relevant text content from the target population. The keywords are then validated, and the content downloaded and analyzed using GPT-3 embedding and manifold reduction. Corpora are then created to fine-tune GPT-2 models to explore latent information via prompt-based queries. These tools allow researchers and practitioners to gain valuable insights into population subgroups online.