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How does the language of 'threat' vary across news domains?: a semi-supervised pipeline for understanding narrative components in news contexts
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0003-4466-1567
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0001-8503-0118
2023 (engelsk)Inngår i: SAIS 2023: 35th Annual Workshop of the Swedish Artificial Intelligence Society / [ed] Håkan Grahn; Anton Borg; Martin Boldt, Swedish Artificial Intelligence Society , 2023, s. 94-99Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

By identifying and characterising the narratives told in news media we can better understand political and societal processes. The problem is challenging from the perspective of natural language processing because it requires a combination of quantitative and qualitative methods. This paper reports on work in progress, which aims to build a human-in-the-loop pipeline for analysing how the variation of narrative themes across different domains, based on topic modelling and word embeddings. As an illustration, we study the language associated with the threat narrative in British news media.

sted, utgiver, år, opplag, sider
Swedish Artificial Intelligence Society , 2023. s. 94-99
Serie
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740
Emneord [en]
topic modelling, natural language processing, narrative analysis, text embeddings
HSV kategori
Forskningsprogram
datorlingvistik; datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-213801DOI: 10.3384/ecp199010ISBN: 978-91-8075-274-9 (digital)OAI: oai:DiVA.org:umu-213801DiVA, id: diva2:1792461
Konferanse
SAIS 2023, 35th Annual Workshop of the Swedish Artificial Intelligence Society, Karlskrona, Sweden, June 12-13, 2023
Tilgjengelig fra: 2023-08-29 Laget: 2023-08-29 Sist oppdatert: 2023-08-29bibliografisk kontrollert

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Ryazanov, IgorBjörklund, Johanna

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