Unveiling Gig Economy Trends via Topic Modeling and Big Data

Abstract

The gig economy, driven by flexible and platform-based work, is reshaping labor markets and employment norms. Understanding public perceptions of this shift is critical for promoting social good and informing equitable policy. This study employs big data analytics and Latent Dirichlet Allocation (LDA) topic modeling to analyze 15,259 tweets collected from the X platform. Seven key themes emerged from the data, including labor precarity, flexibility, algorithmic control, platform accountability, gender disparities, and worker rights. While some users emphasized autonomy and new income opportunities, most expressed concerns about job insecurity, lack of protections, and digital exploitation. These findings offer real-time insights into how gig work is discussed and contested in public discourse. The study highlights how social media analytics can inform labor policy, guide platform regulation, and support advocacy efforts aimed at building a fairer and more resilient gig economy.

Keywords

• gig economy
• big data
• natural language processing
• Latent Dirichlet Allocation algorithm
• topic modeling
• social media analysis