PhD in Political Sciences / Computational Social Sciences (M/F)

Workplace : PARIS

Date of publication : 04 April 2024

Type of Contract : PhD Student contract / Thesis offer

Contract Period : 36 months

Start date of the thesis : 1 October 2024

Proportion of work : Full time

Remuneration : 2 135,00 € gross monthly

 

Thesis topic

An important part of comparative political analysis is based on the position of parties on the political spectrum: right-left axis, attitudes towards immigration, redistribution or even environmental protection. Conventional approaches to estimating party positions include the use of "expert surveys" in which political science experts provide individual subjective estimates (rating each party on Likert scales for different policy topics, and including the Chapel Hill Expert Survey or the Global Party Survey are well-known examples). Alternatively, other approaches rely on the texts of party manifestos through annotations or NLP methods (for example, by automatically identifying named topics and positive attitudes or negative towards them). These important resources require, however, in their construction, considerable resources and limit the temporal resolution available for longitudinal studies. A third line of approaches – ideological spatialization methods (ideological scaling or ideal-point estimation) – exploit traces of behavioral data. These approaches are based on Item-Response Theory frameworks to position individuals on the parameter spaces of choice models, from which the dimensions of attitudes towards political issues are then derived. In the 2010s, these approaches expanded to include social media data, positioning political elites and large populations of users based on how they perceive the positions of political elites, but on dimensions tailored to the political analysis in the American context. The European context is, however, different in terms of dimensionality: different studies (whether at the level of the population or of political elites) show the need to consider multiple issue and ideology dimensions to explain the different choice data. The need for this multidimensionality imposes a series of conceptual and methodological challenges.



In this project, the PhD student will work with social media and political survey data to propose methods to produce and assess political position estimates based on behavioral data. A first aim of this study is to explore the extent to which social media data can be leveraged to produce issue and ideology positions for political parties in a way that allows for comparative studies across Europe, and for granular longitudinal studies. Building on the responses to this methodological challenge, the PhD student will engage in research leveraging social media data and computational methods producing comparative analysis, focusing on the evolution of polarization and the dimensionality of political competition in Europe.



Desired profile



Political science students with strong methodological background

Students with strong computational/data knowledge and skills are particularly encouraged to apply



Date of start: 1 October 2024

Duration of the contract: 3 years.

Work context

This thesis project will be developed using data from the Politoscope of the Paris Institut of Complex Systems (Institut des Systèmes Complexes de Paris Ile-de-France, ISC-PIF) and the European Polarisation Observatory of Sciences Po, which represent a privileged source of data on social networks.



The data from the European Polarization Observatory includes user interaction data on Twitter with political elites from 30 European countries. These data provide a valuable source for comparative studies. The data from the Politoscope project is centered on the activity of Twitter in France, encompassing a decade of collection, enabling an important longitudinal dimension of the project.



The PhD student will work physically in Paris, between the ISC-PIF and Sciences Po. The scientific ecosystem for the development of the thesis offer a world-leading center in political sciences, as well as a diverse interdisciplinary environment integrating researchers in mathematics, computer sciences, sociology, and connections with researchers working in platform regulation and policy-making.

Advisors

 The doctoral student will be co-advised by Pedro Ramaciotti (CNRS Researcher affiliated to the Paris Institute of Complex Systems and Sciences Po) and Jan Rovny (Professor of Political Sciences at Sciences Po).



If you have questions, please contact pedro.ramaciotti-morales@cnrs.fr

Application

Please send us a CV and a letter explaining you research interests and how they align with the project. Include in the CV (for instance, as annexes or using links) examples of code you've written if any (e.g. github repositories) and writing examples (e.g., publications, master thesis, or any text you're particularly proud of).