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1
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
., Giovanna Maria Dora
;
Bhattacharya, Rohit
. - : Underline Science Inc., 2022
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. ...
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Computational Linguistics
;
Condensed Matter Physics
;
Data Acquisition
;
FOS Mathematics
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FOS Physical sciences
;
Information and Knowledge Engineering
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Machine Learning
;
Neural Network
;
Semantics
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Statistics
;
Translation Studies
URL:
https://underline.io/lecture/29888-characterizing-news-portrayal-of-civil-unrest-in-hong-kong
https://dx.doi.org/10.48448/yyrf-j853
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