Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998-2020

Abstract

We apply statistical techniques from natural language processing to a collection of Western and Hong Kong-based English-language newspaper articles spanning the years 1998- 2020, studying the difference and evolution of its portrayal. We observe that both content and attitudes differ betweenWestern and Hong Kong-based sources. ANOVA on keyword frequencies reveals that Hong Kong-based papers discuss protests and democracy less often. Topic modeling detects salient aspects of protests and shows that Hong Kong-based papers made fewer references to police violence during the Anti-Extradition Law Amendment Bill Movement. Diachronic shifts in word embedding neighborhoods reveal a shift in the characterization of salient keywords once the Movement emerged. Together, these raise questions about the existence of anodyne reporting from Hong Kong-based media. Likewise, they illustrate the importance of sample selection for protest event analysis.