As an experiment, we decided to see what similarity between users looks like and whether information about this similarity is interesting to users.
In Sonar, users are given the opportunity to answer short (one-question) surveys to test the similarity of their views with the positions of different political parties.
We start with a multidimensional space. Each survey or poll is one dimension. However, such a solution is not very practical (due to laboriousness and difficulty in imagining it). Therefore we decided to use PCA (Principal Component Analysis), which is a classical algorithm from dimension reduction family. It transforms our data in such a way that only five dimensions remain, with the least possible loss of information in relation to their original form.
Our contribution to Data Driven Journalism: we were the first in the world to create an algorithm to visualize the similarity between users of a news service based on polls and surveys. We transformed the extracted data to embed it in five dimensions, with as little loss of information from its original form as possible.
