Here, we use such data - the
motions of Swedish parties between 1971 and 2015 - to see if they can be used
to: a. establish salient topics, b. observe and explain changes in saliency,
and c. if one party's change in saliency can be used to ``predict'' the change
in another's. To establish the topics, we use a Structural Topic Model (STM),
as this allows us to include additional parameters such as year of publication
and the party of the author. Apart from generating the topics, this model also
allows us to study relations and dependencies between topics as well as
identify if certain words and terms were more popular for some parties than for
others. Doing so, we identify fifteen topics that are relatively stable over
time, and whose popularity can be matched with various historical events. Also,
we find that while some topics do predict changes, others do not show such
effects. Given this, we conclude with a brief discussion on the promises and
pitfalls of using large, free open data and the opportunities and challenges it
generates for political science.
Information
about the speaker
Bastiaan
Bruinsma is currently a post-doc at Chalmers. His research interests include
electoral behaviour, topic models, and automated text analysis. In addition, he
previously published on topics related to Voting Advice Applications and
dimensionality reduction and nationalism.
The event
is hybrid: zoom-link, password: mondays23
Category
Seminar
Location:
Starts:
21 November, 2022, 14:00
Ends:
21 November, 2022, 15:00