Identifying the Public's Beliefs about Generative Artificial Intelligence: A Big Data Approach

In an era where generative AI (GenAI) is reshaping industries, public understanding of this phenomenon remains limited. This study addresses this gap by analysing public beliefs about GenAI using the Technology Acceptance Model (TAM) and Diffusion of Innovations Theory (DOI) as frameworks. We adopted a big-data approach, utilising machine-learning techniques to analyse 21,817 public comments extracted from an initial set of 32,707 on 44 YouTube videos discussing GenAI. Our investigation surfaced six pivotal themes: concerns over job and economic impacts, GenAI's potential to revolutionise problem-solving, its perceived shortcomings in creativity and emotional intelligence, the proliferation of misinformation, existential risks, and privacy decay. Emotion analysis showed that negative emotions dominated at 58.46%, including anger (22.85%) and disgust (17.26%). Sentiment analysis echoed this negativity, with 70% negative. The triangulation of thematic, emotional, and sentiment analyses highlighted a polarised public stance: recognition of GenAI's transformative potential is tempered by significant concerns about its implications. The findings offer actionable insights for engineering managers and policymakers. Strategies such as awareness-building, transparency, public engagement, balanced communication, governance, and human-centred development can address polarisation and build trust. Ongoing research into public opinion remains essential for aligning technological advancements with societal expectations and acceptance.