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Data-Driven Campaigning in Data-Dense Small Multiparty Systems: A Party-Level Analysis

Authors: Anamaria Dutceac Segesten; Linn Sandberg;

Data-Driven Campaigning in Data-Dense Small Multiparty Systems: A Party-Level Analysis

Abstract

This study examines data-driven campaign (DDC) practices in Sweden. We explore the extent of data-driven practices adopted in Swedish political campaigns, and parties’ motivations to adopt them. Since this is a comparison of domestic parties, we test the importance of four party-level factors—resources, structure, attitudes toward data use, and ideology—using extensive interviews with key campaign managers in Sweden during the 2022 election year. Our results show that the differences among the eight parties studied are rather small, and that systemic factors are more important than party variables to explain the adoption of data-driven approaches. Zooming in on these finer differences we distinguish between top DDC adopters (Social Democrats, Center, and Conservatives) and a lower tier with lower levels of DDC implementation. To explain the differences between the two tiers, we find that economic resources are important, with richer parties being more advanced in DDC use. Party structure, attitudes to data, and ideology do not affect the likelihood of a Swedish party using data analytics in their strategic decision-making. Instead, we suggest party type (catch-all vs. niche) is a potentially more useful party-level factor in explaining variation.

Country
Norway
Keywords

election campaigns, political parties, 330, democracy, Communication. Mass media, political communication, data analytics, P87-96

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
gold
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