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Measuring Issue Ownership using Word Embeddings
RISE - Research Institutes of Sweden, ICT, SICS.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-5100-0535
2018 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Sentiment and topic analysis are commonmethods used for social media monitoring.Essentially, these methods answers questionssuch as, “what is being talked about, regardingX”, and “what do people feel, regarding X”.In this paper, we investigate another venue forsocial media monitoring, namely issue ownership and agenda setting, which are conceptsfrom political science that have been used toexplain voter choice and electoral outcomes.We argue that issue alignment and agenda setting can be seen as a kind of semantic sourcesimilarity of the kind “how similar is sourceA to issue owner P, when talking about issue X”, and as such can be measured usingword/document embedding techniques. Wepresent work in progress towards measuringthat kind of conditioned similarity, and introduce a new notion of similarity for predictive embeddings. We then test this methodby measuring the similarity between politically aligned media and political pparties, conditioned on bloc-specific issues.

Place, publisher, year, edition, pages
2018. article id W18-6221
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-37583OAI: oai:DiVA.org:ri-37583DiVA, id: diva2:1282654
Conference
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Available from: 2019-01-25 Created: 2019-01-25 Last updated: 2019-01-25Bibliographically approved

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http://aclweb.org/anthology/W18-6221

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Sahlgren, Magnus

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