The Rise of Artificial Intelligence Phobia! Unveiling News-Driven Spread of AI Fear Sentiment Using ML, NLP, and LLMs

Contemporary public discourse surrounding artificial intelligence (AI) often displays disproportionate fear and confusion relative to AI’s actual potential. This study examines how the use of alarmist and fear-inducing language by news media contributes to negative public perceptions of AI. Nearly 70,000 AI-related news headlines were analyzed using natural language processing (NLP), machine learning (ML), and large language models (LLMs) to identify dominant themes and sentiment patterns. The theoretical framework draws on existing literature that posits the power of fear-inducing headlines to influence public perception and behavior, even when such headlines represent a relatively small proportion of total coverage. This research applies topic modeling and fear sentiment classification using BERT, LLaMA, and Mistral, alongside supervised ML techniques. The findings show a persistent presence of emotionally negative and fear-laden language in AI news coverage. This portrayal of AI as dangerous to humans or as an existential threat profoundly shapes public perception, fueling AI phobia that leads to behavioral resistance toward AI, which is ultimately detrimental to the science of AI. Furthermore, this can have an adverse impact on AI policies and regulations, leading to a stunted growth environment for AI. The study concludes with implications and recommendations to counter fear-driven narratives and suggests ways to improve public understanding of AI through responsible news media coverage, broad AI education, democratization of AI resources, and the drawing of clear distinctions between AI as a science versus commercial AI applications, to promote enhanced fact-based mass engagement with AI while preserving human dignity and agency.