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SSRN Electronic Journal
Article . 2014 . Peer-reviewed
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https://dx.doi.org/10.24406/pu...
Other literature type . 2014
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Conference object . 2014
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Research . 2014
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Analysing Party Preferences Using Google Trends

Authors: Mirko Seithe; Lena Calahorrano;

Analysing Party Preferences Using Google Trends

Abstract

The formation of party preferences is a complex and not yet fully understood process based on a number of factors. This process, which is of great interest for both social and political science, is usually studied using questionnaire data which has proven to be a very reliable yet often costly and limited approach. Advances in technology and the rise of the internet as a primary information source for many people have created a new approach to keep track of people s interests. The major gateways to the internet s information are the so-called search engines, and Google, arguably the most commonly used search engine, allows scientists to tap the vast source of information generated by its users search queries. In this paper we describe how this data source can be used to estimate the effect of different issues on party preferences using German voters and the German party system as an example. We find that using data provided by Google Trends can lead to a variety of interesting and occasionally counterintuitive insights into peoples party preferences.

Keywords

ddc:330, Google Trends, voting behaviour, D72, C80, search volume, H00, voting behaviour, issue ownership, search volume, Google Trends, issue ownership, jel: jel:C80, jel: jel:D72, jel: jel:H00

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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
bronze