  We study the power of Twitter events to predict consumer sales events by
analysing sales for 75 companies from the retail sector and over 150 million
tweets mentioning those companies along with their sentiment. We suggest an
approach for events identification on Twitter extending existing methodologies
of event study. We also propose a robust method for clustering Twitter events
into different types based on their shape, which captures the varying dynamics
of information propagation through the social network. We provide empirical
evidence that through events differentiation based on their shape we can
clearly identify types of Twitter events that have a more significant power to
predict spikes in sales than the aggregated Twitter signal.
