Predicting Cross-lingual Trends in Microblogs

AbstractTrends on microblogs often transcend linguistic boundaries, evolving into global phenomena with significant societal and economic impact. This paper introduces and tackles the novel predictive task of forecasting which microblog trends will cross linguistic boundaries to become popular in other languages, and when. While crucial for proactive global monitoring and marketing, this area has been under-explored. We introduce a methodology to overcome the challenge of cross-lingual trend identification by automatically constructing a dataset using Wikipedia’s inter-language links. We then propose a prediction model that leverages a rich feature set, including not only temporal frequency but also microblog content and external knowledge signals from Wikipedia. Our approach significantly outperforms existing trend prediction methods and LLM-based approaches, achieving an improvement of up to 4% in F1-score, enabling the forecast of cross-lingual trends before they emerge in a new language.