Artificial Intelligence (AI) is reshaping economic landscapes, generating both
enthusiasm and fear regarding its impact on jobs. While experts emphasize AI’s
potential to enhance productivity and create new roles, public sentiment largely reflects
concerns about job displacement and automation. Despite extensive research on AI’s
labor market effects, a gap remains in studies that integrate public sentiment analysis,
economic data, and media framing. This research addresses this gap by analyzing
11,708 YouTube comments on AI-related videos using sentiment analysis with the
RoBERTa model, thematic extraction via ChatGPT, and linguistic analysis through
Voyant Tools. Findings reveal that over 50% of public discourse expresses negative
sentiments, with concerns centering on job loss, automation, and loss of human control.
The most frequently discussed sectors include technology, healthcare, and finance.
Media narratives were found to play a critical role in amplifying fear through
sensationalist portrayals, often exaggerating AI’s disruptive impact. Public perception
significantly diverges from expert discourse, which focuses on AI’s long-term
economic benefits and job transformation rather than immediate displacement. This
disconnect highlights the need for proactive strategies to address misinformation and
workforce adaptation. Bridging the gap requires balanced media reporting, reskilling
programs, and transparent policy frameworks that emphasize augmentation over
automation. Policymakers must ensure equitable AI integration, while businesses
should prioritize human-AI collaboration. Educational institutions must equip
individuals with AI literacy to prepare them for evolving job markets. Without such
interventions, AI’s potential to drive economic growth may be hindered by public
resistance fueled by fear and uncertainty.
| Date of Award | 2025 |
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| Original language | American English |
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| Awarding Institution | - HBKU College of Humanities and Social Science
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AI And Job Futures: Exploring Public Sentiments On Technological Impact In YouTube Comments
Abdullah, G. (Author). 2025
Student thesis: Master's Dissertation