Detecting Cross-cultural Differences Using a Multilingual Topic Model

Abstract

Understanding cross-cultural differences has important implications for world affairs and many aspects of the life of society. Yet, the majority of text-mining methods to date focus on the analysis of monolingual texts. In contrast, we present a statistical model that simultaneously learns a set of common topics from multilingual, non-parallel data and automatically discovers the differences in perspectives in these topics across linguistic communities. We perform a behavioural evaluation of a subset of the differences identified by our model in English and Spanish to investigate their psychological validity.