Multilingual user perceptions analysis from twitter using zero shot learning for border control technologies

Abstract Online social networks such as Twitter, Facebook, Instagram, and Reddit have transformed communications by enabling users to share their opinions and perceptions. The vast amount of user-generated content on these platforms poses significant challenges for manual analysis. Advances in artificial intelligence, particularly transformer-based models such as BERT and GPT, have improved the processing of multilingual data for tasks such as text classification, sentiment analysis, and emotion analysis. However, these models often require extensive task-specific training and high-quality labeled data, making them impractical for multilingual contexts. This study addresses these limitations by leveraging zero-shot learning with transformer-based models, which eliminate the need for task-specific training and can classify new data into unseen classes without manual annotation. The use case for this study is border control technologies (BCTs), a hot topic following the European Union commission’s “Smart Borders Package" aimed at improving border crossing points efficiency and security. The major contribution of this study lies in introducing a novel framework to explore the multilingual user perceptions, focusing on BCTs using an innovative “user perception extraction architecture" for analyzing multilingual perceptions from Twitter. This architecture enables modular, scalable, and domain-independent analysis that is adaptable to various emerging technologies and domains beyond BCTs. Furthermore, this study compiles a unique dataset of 90,789 multilingual tweets related to BCTs from 2008 to 2022, providing valuable insights into public perceptions for BCTs. The findings reveal dynamic trends in user perceptions influenced by geopolitical events and policy changes, offering actionable insights for policymakers, researchers, and developers. By contextualizing these findings, this study equips stakeholders with new knowledge to bridge the gap between public concerns and adoption of BCTs.