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What’s the matter? A text mining analysis of political topics and user engagement on politicians’ Facebook pages during the 2018 Hungarian general election campaign
94-123.Views:8The research investigates the way users interact with leading topics of the 2018 Hungarian
general election campaign on candidates’ Facebook pages. It expects that the prominent
(immigration, corruption) and campaign-related topics generate more user engagement, while
policy topics and mobilization content are less interacted. It also tests the theory of issue ownership
in relation with user engagement. These expectations are tested on a dataset that includes all
posts (38030 posts) posted by all candidates during the campaign (511 candidates). Topics
are identified by text mining methods. The study demonstrates that corruption, development
policy and campaign are highly engaged topics, while immigration was more interacted only on
opposition politicians’ pages since the followers of pro-government candidates engage less with
immigration-related content. The most surprising result is that a reversed issue ownership effect
can be detected since politicians are generally less successful with their own topics. -
Does the corruption affect to the voters? – a Bayesian econometric analysis
25-66Views:8The study examines the agenda-setting aspirations of Hungarian political life between 2010
and 2016 from a corruption research perspective. Using the available data, we estimate, based
on the monthly data series of a six-year period, using different statistical methods, whether the
allocation of European Union funds used as a proxy for corruption had an impact on the support
of the ruling party. The results of the applied Bayesian vector autoregression do not provide
evidence for the hypothesis that the increase in corruption associated with the increase in EU
subsidies reduces the popularity of the ruling party among the entire voting population.
