Changing Alliances in Trump’s White House: Networks and Elite Politics
Who has Trump’s ear during the election campaign and in the White House, and are they able to influence his policy? Who has formed an alliance with whom, and who is about to get fired? The media has reported extensively on the internal politics of Trump’s inner circle, but no one has tried to gather this information chronologically, examined differences in the narrative between news outlets, and checked systematically how often the predictions turned out to be right.
The project will gather information about the relationships between leading members of the Trump campaign and top government officials in the Trump administration as portrayed in different newspapers, as well as information on their policy positions, and predictions about personnel changes. It will then compare the networks of different news outlets, and check how often their predictions and information turn out to be correct – by verifying them against later outcomes and information from (future) biographies and court documents. An additional goal is to apply social network analysis to the changing network of alliances and enmities, in order to explain which policies will be chosen, and who might get hired or fired. At a later stage, the project will also examine the networks connecting the journalists to the political actors through/as sources. The goal there is to explore if it is possible to identify the sources from the network structure, and examine the process of strategic leaking by political actors.
We will also experiment with automating the data-gathering process using natural language processing.
The results of this projects will hopefully also shed light on elite politics and reporting thereof in more opaque regimes such as the P.R. of China, where we are much less likely to receive definitive evidence to verify rumors about relationships and policy stances any time soon.
UROP1000 UROP1100 UROP2100
You will read newspaper articles on the Trump campaign and the White House and record information about relationships between the actors mentioned, about their policy positions, and predictions about personnel changes.
You will then learn how to construct networks from this data, how to analyze it using social network concepts and tools, and write a short report on the changes that the network undergoes over time.
If you have a background in programming, you can also help develop a machine-learning algorithm to automate the data-collection.
Applicant's Learning Objectives:
You will learn the rules of good journalism: how do journalists report on politically contested information from sources that prefer to remain anonymous? How can we tell the difference between serious journalism and web sites that simply spread rumours?
You will also learn how to systematically code information from narrative texts by turning it into more structured data, and how to analyse the latter using social network analysis.
And you will of course learn a lot about the actors and the internal politics of the Trump campaign and the current US government.