Can cross-lingual information cascades be predicted on twitter?

Abstract

Social network services (SNSs) have provided many opportunities for sharing information and knowledge in various languages due to their international popularity. Understanding the information flow between different countries and languages on SNSs can not only provide better insights into global connectivity and sociolinguistics, but is also beneficial for practical applications such as globally-influential event detection and global marketing. In this study, we characterized and attempted to detect influential cross-lingual information cascades on Twitter. With a large-scale Twitter dataset, we conducted statistical analysis of the growth and language distribution of information cascades. Based on this analysis, we propose a feature-based model to detect influential cross-lingual information cascades and show its effectiveness in predicting the growth and language distribution of cascades in the early stage.