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    <titleInfo>
        <title>Measuring Issue Ownership using Word Embeddings</title>
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        <namePart type="given">Amaru</namePart>
        <namePart type="family">Cuba Gyllensten</namePart>
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            <title>Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
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            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Balahur</namePart>
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            <namePart type="given">Saif</namePart>
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    <abstract>Sentiment and topic analysis are common methods used for social media monitoring. Essentially, these methods answers questions such as, “what is being talked about, regarding X”, and “what do people feel, regarding X”. In this paper, we investigate another venue for social media monitoring, namely issue ownership and agenda setting, which are concepts from political science that have been used to explain voter choice and electoral outcomes. We argue that issue alignment and agenda setting can be seen as a kind of semantic source similarity of the kind “how similar is source A to issue owner P, when talking about issue X”, and as such can be measured using word/document embedding techniques. We present work in progress towards measuring that kind of conditioned similarity, and introduce a new notion of similarity for predictive embeddings. We then test this method by measuring the similarity between politically aligned media and political parties, conditioned on bloc-specific issues.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6221</identifier>
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%0 Conference Proceedings
%T Measuring Issue Ownership using Word Embeddings
%A Cuba Gyllensten, Amaru
%A Sahlgren, Magnus
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F cuba-gyllensten-sahlgren-2018-measuring
%X Sentiment and topic analysis are common methods used for social media monitoring. Essentially, these methods answers questions such as, “what is being talked about, regarding X”, and “what do people feel, regarding X”. In this paper, we investigate another venue for social media monitoring, namely issue ownership and agenda setting, which are concepts from political science that have been used to explain voter choice and electoral outcomes. We argue that issue alignment and agenda setting can be seen as a kind of semantic source similarity of the kind “how similar is source A to issue owner P, when talking about issue X”, and as such can be measured using word/document embedding techniques. We present work in progress towards measuring that kind of conditioned similarity, and introduce a new notion of similarity for predictive embeddings. We then test this method by measuring the similarity between politically aligned media and political parties, conditioned on bloc-specific issues.
%R 10.18653/v1/W18-6221
%U https://aclanthology.org/W18-6221
%U https://doi.org/10.18653/v1/W18-6221
%P 149-155

Markdown (Informal)

[Measuring Issue Ownership using Word Embeddings](https://aclanthology.org/W18-6221) (Cuba Gyllensten & Sahlgren, WASSA 2018)

• Measuring Issue Ownership using Word Embeddings (Cuba Gyllensten & Sahlgren, WASSA 2018)

ACL

• Amaru Cuba Gyllensten and Magnus Sahlgren. 2018. Measuring Issue Ownership using Word Embeddings. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 149–155, Brussels, Belgium. Association for Computational Linguistics.