2021/02/24 by Autumn Toney, Toney, Autumn
Computer Science · Engineering · Psychology · Social Sciences · #Artificial intelligence #Computation and Language (cs.CL) #Computational and Text Analysis Methods #Computer science #Data science #Database #Engineering #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Linguistics #Machine learning #Misinformation and Its Impacts #Natural language processing #Perception #Psychology #Scalability #Scale (ratio) #Word (group theory) #Work (physics) #cs.CL
paper · pdf · doi:10.48550/arxiv.2102.12516
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/02/24 · openalex publication_date 2021/02/24 · arxiv updated 2021/02/26 · openalex created_date 2021/03/01 · openalex updated_date 2026/07/28
This work presents a large-scale analysis of artificial intelligence (AI) and machine learning (ML) references within news articles and scientific publications between 2011 and 2019. We implement word association measurements that automatically identify shifts in language co-occurring with AI/ML and quantify the strength of these word associations. Our results highlight the evolution of perceptions and definitions around AI/ML and detect emerging application areas, models, and systems (e.g., blockchain and cybersecurity). Recent small-scale, manual studies have explored AI/ML discourse within the general public, the policymaker community, and researcher community, but are limited in their scalability and longevity. Our methods provide new views into public perceptions and subject-area expert discussions of AI/ML and greatly exceed the explanative power of prior work.