@inproceedings{scharf-etal-2021-characterizing,
    title = "Characterizing News Portrayal of Civil Unrest in {H}ong {K}ong, 1998{--}2020",
    author = "Scharf, James  and
      McCarthy, Arya D.  and
      Dore, Giovanna Maria Dora",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali},
    booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.case-1.7/",
    doi = "10.18653/v1/2021.case-1.7",
    pages = "43--52",
    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."
}