The Keyword Explorer Suite: A Toolkit for Understanding Online Populations

Autor:	Feldman, Philip, Pan, Shimei, Foulds, James R.
Rok vydání:	2023
Předmět:	Computer Science - Human-Computer Interaction H.5.2 H.1.2 I.2.7
Druh dokumentu:	Working Paper
Popis:	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. Source code at https://github.com/pgfeldman/KeywordExplorer Comment: 6 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.05198 Zobrazit plný text záznamu View this record from Arxiv