Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages

The advent of generative AI tools has had a profound impact on societies globally, transcending geographical boundaries. Understanding these tools' global reception and utilization is crucial for service providers and policymakers in shaping future policies. Therefore, to unravel the perceptions and engagements of individuals within diverse linguistic communities with regard to generative AI tools, we extensively analyzed over 6.8 million tweets in 14 different languages. Our findings reveal a global trend in the perception of generative AI, accompanied by language-specific nuances. While sentiments toward these tools vary significantly across languages, there is a prevalent positive inclination toward Image tools and a negative one toward Chat tools. Notably, the ban of ChatGPT in Italy led to a sentiment decline and initiated discussions across languages. Furthermore, we established a taxonomy for interactions with chatbots, creating a framework for social analysis underscoring variations in generative AI usage among linguistic communities. We find that the Chinese community predominantly employs chatbots as substitutes for search, while the Italian community tends to use chatbots for tasks such as problem-solving assistance and engaging in entertainment or creative tasks. Our research provides a robust foundation for further explorations of the social dynamics surrounding generative AI tools and offers invaluable insights for decision-makers in policy, technology, and education.

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Publication Details

Journal
Proceedings of the International AAAI Conference on Web and Social Media
Published
2025-06-07
DOI
https://doi.org/10.1609/icwsm.v19i1.35872
Citations
3
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
7.44

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article

Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages

Yasuko Matsubara, Taichi Murayama, Yasushi Sakurai, Kunihiro Miyazaki
3 citations
Proceedings of the International AAAI Conference on Web and Social Media
Topic Modeling
7.44
article

Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages

Yasuko Matsubara, Taichi Murayama, Yasushi Sakurai, Kunihiro Miyazaki
article en
3 citations

Abstract

The advent of generative AI tools has had a profound impact on societies globally, transcending geographical boundaries. Understanding these tools' global reception and utilization is crucial for service providers and policymakers in shaping future policies. Therefore, to unravel the perceptions and engagements of individuals within diverse linguistic communities with regard to generative AI tools, we extensively analyzed over 6.8 million tweets in 14 different languages. Our findings reveal a global trend in the perception of generative AI, accompanied by language-specific nuances. While sentiments toward these tools vary significantly across languages, there is a prevalent positive inclination toward Image tools and a negative one toward Chat tools. Notably, the ban of ChatGPT in Italy led to a sentiment decline and initiated discussions across languages. Furthermore, we established a taxonomy for interactions with chatbots, creating a framework for social analysis underscoring variations in generative AI usage among linguistic communities. We find that the Chinese community predominantly employs chatbots as substitutes for search, while the Italian community tends to use chatbots for tasks such as problem-solving assistance and engaging in entertainment or creative tasks. Our research provides a robust foundation for further explorations of the social dynamics surrounding generative AI tools and offers invaluable insights for decision-makers in policy, technology, and education.

Proceedings of the International AAAI Conference on Web and Social MediaVol. 19
Yokohama National University (JP), Sanken Electric (Japan) (JP), The University of Tokyo (JP), The University of Osaka (JP)
Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology, Research Institute of Science and Technology for Society
Openalex Percentile: Top 3%
Topic Modeling
7.44
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