The Gig's Up: How ChatGPT Stacks Up Against Quora on Gig Economy Insights
Thomas Lancaster
TL;DR
The paper investigates whether ChatGPT can replicate Quora-style Q&A content on the gig economy. It uses ChatGPT as a research assistant to collect Quora data and to generate AI-simulated questions and answers, applying content analysis with eight predefined categories to compare sources. Key findings show Quora responses tend to be personal and experience-based, while ChatGPT produces concept-based, broad analyses; this highlights both complementary strengths and limitations of AI-generated content. The study proposes a generalizable comparative methodology for evaluating AI versus human-produced knowledge across domains, with practical implications for researchers and educators on when to rely on each source.
Abstract
Generative AI is changing the way in which humans seek to find answers to questions in different fields including on the gig economy and labour markets, but there is limited information available about closely ChatGPT simulated output matches that obtainable from existing question and answer platforms. This paper uses ChatGPT as a research assistant to explore how far ChatGPT can replicate Quora question and answers, using data from the gig economy as an indicative case study. The results from content analysis suggest that Quora is likely to be asked questions from users looking to make money and answers are likely to include personal experiences and examples. ChatGPT simulated versions are less personal and more concept-based, including considerations on employment implications and labour rights. It appears therefore that generative AI simulates only part of what a human would want in their answers relating to the gig economy. The paper proposes that a similar comparative methodology would also be useful across other research fields to help in establishing the best real world uses of generative AI.