Using Social Media to Forecast Electoral Results: A Review of the State of the Art

Italian Journal of Applied Statistics – Statistica Applicata

Co-authors: Luigi Curini & Stefano M. Iacus

Replication material: andreaceron.com/publications

Acknowledgments: Voices from the Blogs for providing data

What is worth remembering:

• Many scholars tried to predict elections using social media
• Some methods are better than others
• Supervised sentiment analysis seems the best choice
• Predictions are more accurate in countries with Proportional Representation

Abstract

The paper discusses the advantages of using Supervised Aggregated Sentiment Analysis (SASA) of social media to forecast electoral results and presents an extension of the ReadMe method (Hopkins and King, 2010), which is particularly suitable to addressing a large number of categories (e.g. parties) providing lower standard errors. We analyze the voting intention of social media users in several elections held between 2011 and 2013 in France, Italy, and the United States. We then compare 80 electoral forecasts made using these or other techniques of data-mining and sentiment analysis. The comparison shows that the choice of the method is crucial. Electoral forecasts are also more accurate in countries with higher Internet penetration and given the presence of electoral systems based on proportional representation.