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

James Scharf, Arya D. McCarthy, Giovanna Maria Dora Dore


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 between Western 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.
Anthology ID:
2021.case-1.7
Volume:
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | CASE | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
43–52
Language:
URL:
https://aclanthology.org/2021.case-1.7
DOI:
10.18653/v1/2021.case-1.7
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.case-1.7.pdf