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        <title>MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media</title>
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    <name type="personal">
        <namePart type="given">Chang</namePart>
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            <title>Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="family">Da San Martino</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Chris</namePart>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
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    <abstract>The way information is generated and disseminated has changed dramatically over the last decade. Identifying the political perspective shaping the way events are discussed in the media becomes more important due to the sharp increase in the number of news outlets and articles. Previous approaches usually only leverage linguistic information. However, news articles attempt to maintain credibility and seem impartial. Therefore, bias is introduced in subtle ways, usually by emphasizing different aspects of the story. In this paper, we propose a novel framework that considers entities mentioned in news articles and external knowledge about them, capturing the bias with respect to those entities. We explore different ways to inject entity information into the text model. Experiments show that our proposed framework achieves significant improvements over the standard text models, and is capable of identifying the difference in news narratives with different perspectives.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nlp4if-1.10</identifier>
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        <date>2021-06</date>
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            <start>66</start>
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%0 Conference Proceedings
%T MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media
%A Li, Chang
%A Goldwasser, Dan
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F li-goldwasser-2021-mean
%X The way information is generated and disseminated has changed dramatically over the last decade. Identifying the political perspective shaping the way events are discussed in the media becomes more important due to the sharp increase in the number of news outlets and articles. Previous approaches usually only leverage linguistic information. However, news articles attempt to maintain credibility and seem impartial. Therefore, bias is introduced in subtle ways, usually by emphasizing different aspects of the story. In this paper, we propose a novel framework that considers entities mentioned in news articles and external knowledge about them, capturing the bias with respect to those entities. We explore different ways to inject entity information into the text model. Experiments show that our proposed framework achieves significant improvements over the standard text models, and is capable of identifying the difference in news narratives with different perspectives.
%R 10.18653/v1/2021.nlp4if-1.10
%U https://aclanthology.org/2021.nlp4if-1.10
%U https://doi.org/10.18653/v1/2021.nlp4if-1.10
%P 66-75

Markdown (Informal)

[MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media](https://aclanthology.org/2021.nlp4if-1.10) (Li & Goldwasser, NLP4IF 2021)

• MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media (Li & Goldwasser, NLP4IF 2021)

ACL

• Chang Li and Dan Goldwasser. 2021. MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 66–75, Online. Association for Computational Linguistics.