# Political Footprints: Political Discourse Analysis using Pre-Trained Word Vectors

## TL;DR

This paper introduces political footprints, a method using pre-trained word vectors for systematic political discourse analysis, demonstrated through case studies on international agreements and U.S. elections.

## Contribution

It presents a novel application of vector space models to political discourse, providing a simple implementation and heuristics for meaningful analysis.

## Key findings

- Political footprints produce meaningful insights into discourse.
- Application to international agreements shows discernible patterns.
- Case studies on elections demonstrate the method's effectiveness.

## Abstract

In this paper, we discuss how machine learning could be used to produce a systematic and more objective political discourse analysis. Political footprints are vector space models (VSMs) applied to political discourse. Each of their vectors represents a word, and is produced by training the English lexicon on large text corpora. This paper presents a simple implementation of political footprints, some heuristics on how to use them, and their application to four cases: the U.N. Kyoto Protocol and Paris Agreement, and two U.S. presidential elections. The reader will be offered a number of reasons to believe that political footprints produce meaningful results, along with some suggestions on how to improve their implementation.