  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.
