Forecasting election results by studying brand importance in online news
This work addresses electoral forecasting for political analysts by providing a relatively fast and easy-to-apply method based on online big data, though it is incremental in nature.
The study tackled the problem of forecasting election results by analyzing brand importance in online news using the semantic brand score, achieving consistent forecasts across four different voting events in Italy.
This study uses the semantic brand score, a novel measure of brand importance in big textual data, to forecast elections based on online news. About 35,000 online news articles were transformed into networks of co-occurring words and analyzed by combining methods and tools from social network analysis and text mining. Forecasts made for four voting events in Italy provided consistent results across different voting systems: a general election, a referendum, and a municipal election in two rounds. This work contributes to the research on electoral forecasting by focusing on predictions based on online big data; it offers new perspectives regarding the textual analysis of online news through a methodology which is relatively fast and easy to apply. This study also suggests the existence of a link between the brand importance of political candidates and parties and electoral results.