Research-Project

This project aims to create and validate a gold standard for a set of machine learning algorithms and language models that measure political toxicity and incivility in the digital sphere.

This project involves a benchmarking and algorithm audit of a number of Large Language Models (LLMs) to measure toxicity and incivility in digital social media during mass protests in Latin American countries.

This project analyses the development of electronic and open government in 345 Chilean municipalities between 2019 and 2021. The main question is: What factors determine the development of e- and open government in Chilean municipalities? To answer this question, we developed an index that combines elements of e-government with transparency indicators for all municipalities in Chile.

This project comprises two data sets on Chilean ministers and undersecretaries in Comma-Separated Values (CSV) format with Unicode encoding UTF-8. The data set on ministers covers between 1990 and 2014, while the set on undersecretaries contains officeholders from 1990 to 2022.

This project addresses the complexity of different political, social, and economic phenomena from a multidisciplinary approach by analysing big data. This project seeks, in particular, to develop and use machine learning applications to detect anomalies in data used by decision-makers and understand current social dynamics and information consumption dynamics. In general, this project aims to study a number of social science research topics using trained algorithms.

This is a Twitter online tracker of the Chilean referendum for a New Constitution in October 2020, which contains merged, daily data sets on #Apruebo and #Rechazo viewpoints on this social media.