Generative Artificial Intelligence Platform for Automating Social Media Posts From Urology Journal Articles: A Cross-Sectional Study and Randomized Assessment
Abstract
Purpose: This cross-sectional study assessed a generative artificial intelligence platform to automate the creation of accurate, appropriate, and compelling social media (SoMe) posts from urological journal articles. Materials and Methods: One hundred SoMe posts from the top 3 journals in urology X (formerly Twitter) profiles were collected from August 2022 to October 2023. A freeware generative pre-trained transformer (GPT) tool was developed to autogenerate SoMe posts, which included title summarization, key findings, pertinent emojis, hashtags, and digital object identifier links to the article. Three physicians independently evaluated GPT-generated posts for achieving tetrafecta of accuracy and appropriateness criteria. Fifteen scenarios were created from 5 randomly selected posts from each journal. Each scenario contained both the original and the GPT-generated post for the same article. Five questions were formulated to investigate the posts' likability, shareability, engagement, understandability, and comprehensiveness. The paired posts were then randomized and presented to blinded academic authors and general public through Amazon Mechanical Turk (AMT) responders for preference evaluation. Results: Median time for post autogeneration was 10.2 seconds (interquartile range 8.5-12.5). Of the 150 rated GPT-generated posts, 115 (76.6%) met the correctness tetrafecta: 144 (96%) accurately summarized the title, 147 (98%) accurately presented the articles’ main findings, 131…
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Evidence Hub reads this as a cross-sectional. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
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