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

2024-05-30

arXiv.org

AI Summary

This study investigates cross-linguistic variations in public perception, usage patterns, and policy responses toward generative AI (e.g., ChatGPT, image generators) across 14 global language communities. Method: Analyzing 6.8 million multilingual tweets, the work integrates multilingual sentiment analysis, topic modeling, social network analysis, and contrastive linguistics–informed normalization to construct the first cross-lingual taxonomy of chatbot interactions. Contribution/Results: It identifies language-specific behavioral paradigms—e.g., Chinese users prioritize alternatives, while Italian users emphasize creative entertainment—and reveals cross-lingual舆情 contagion triggered by policy events (e.g., Italy’s ChatGPT ban). Image-generation tools receive consistently positive sentiment, whereas conversational agents elicit predominantly negative affect. Systematic differences emerge across all 14 communities in attitudinal valence, functional application, and policy sensitivity. The study delivers both empirical foundations and methodological innovations for global AI governance.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• Analyze global perceptions of generative AI across 14 languages
• Compare sentiment variations toward AI tools in different linguistic communities
• Establish a taxonomy for chatbot interactions among diverse users

Innovation

• Analyzed 6.8M tweets in 14 languages
• Established chatbot interaction taxonomy framework
• Revealed language-specific AI sentiment trends