The research fellow’s tasks will be to make the automated solution for protest event analysis available to a broader social science audience. This includes:
- The development of a user-friendly R package that wraps the original Python code, in which the classifier was originally developed by a computational linguist. The latter would continue to advise the project.
- The setting up of a data-base for the protest and electoral data and to make it available for the social science audience.
- The management of an nlp-pipeline that allows to regularly update the protest event data based on newswire data from LexisNexis.
- Analysis of the data for future publications
The task includes IT infrastructure management, work with LexisNexis API, as well as machine learning for the selection of newspaper articles.
Besides strong and independent programming competencies in R and Python, the post-doc should have a strong methodological understanding of quantitative text analysis and be able to communicate this understanding to a broader social science audience. Preferably the successful candidate should have a PhD in social sciences and the required programming competencies, but candidates with a PhD in computational linguistics and an interest in social sciences are also welcomed.
Place of work: Florence, Italy
Starting time: 1 February or 1 March 2019 until July 2020, full time
More info and the application form can be found here.