Political Disaffection: a case study on the Italian Twitter community

TL;DR

This study analyzes political disaffection in Italy by examining Twitter data with machine learning, validating it against surveys and correlating peaks with major political news.

Contribution

It introduces a method to measure political disaffection using Twitter data and validates it through comparison with public opinion surveys and news events.

Findings

Twitter-based disaffection signals align with survey data

Major political news correlates with peaks in disaffection time-series

Twitter data effectively captures political sentiment trends

Abstract

In our work we analyse the political disaffection or "the subjective feeling of powerlessness, cynicism, and lack of confidence in the political process, politicians, and democratic institutions, but with no questioning of the political regime" by exploiting Twitter data through machine learning techniques. In order to validate the quality of the time-series generated by the Twitter data, we highlight the relations of these data with political disaffection as measured by means of public opinion surveys. Moreover, we show that important political news of Italian newspapers are often correlated with the highest peaks of the produced time-series.