<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="niven-kao-2020-measuring">
    <titleInfo>
        <title>Measuring Alignment to Authoritarian State Media as Framing Bias</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Niven</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hung-Yu</namePart>
        <namePart type="family">Kao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="family">Da San Martino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chris</namePart>
            <namePart type="family">Brew</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="given">Luca</namePart>
            <namePart type="family">Ciampaglia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chris</namePart>
            <namePart type="family">Leberknight</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee on Computational Linguistics (ICCL)</publisher>
            <place>
                <placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We introduce what is to the best of our knowledge a new task in natural language processing: measuring alignment to authoritarian state media. We operationalize alignment in terms of sociological definitions of media bias. We take as a case study the alignment of four Taiwanese media outlets to the Chinese Communist Party state media. We present the results of an initial investigation using the frequency of words in psychologically meaningful categories. Our findings suggest that the chosen word categories correlate with framing choices. We develop a calculation method that yields reasonable results for measuring alignment, agreeing well with the known labels. We confirm that our method does capture event selection bias, but whether it captures framing bias requires further investigation.</abstract>
    <identifier type="citekey">niven-kao-2020-measuring</identifier>
    <location>
        <url>https://aclanthology.org/2020.nlp4if-1.2</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>11</start>
            <end>21</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Measuring Alignment to Authoritarian State Media as Framing Bias
%A Niven, Timothy
%A Kao, Hung-Yu
%Y Da San Martino, Giovanni
%Y Brew, Chris
%Y Ciampaglia, Giovanni Luca
%Y Feldman, Anna
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F niven-kao-2020-measuring
%X We introduce what is to the best of our knowledge a new task in natural language processing: measuring alignment to authoritarian state media. We operationalize alignment in terms of sociological definitions of media bias. We take as a case study the alignment of four Taiwanese media outlets to the Chinese Communist Party state media. We present the results of an initial investigation using the frequency of words in psychologically meaningful categories. Our findings suggest that the chosen word categories correlate with framing choices. We develop a calculation method that yields reasonable results for measuring alignment, agreeing well with the known labels. We confirm that our method does capture event selection bias, but whether it captures framing bias requires further investigation.
%U https://aclanthology.org/2020.nlp4if-1.2
%P 11-21

Markdown (Informal)

[Measuring Alignment to Authoritarian State Media as Framing Bias](https://aclanthology.org/2020.nlp4if-1.2) (Niven & Kao, NLP4IF 2020)

• Measuring Alignment to Authoritarian State Media as Framing Bias (Niven & Kao, NLP4IF 2020)

ACL

• Timothy Niven and Hung-Yu Kao. 2020. Measuring Alignment to Authoritarian State Media as Framing Bias. In Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 11–21, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).