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We study the power of Twitter events to predict consumer sales events byanalysing sales for 75 companies from the retail sector and over 150 milliontweets mentioning those companies along with their sentiment. We suggest anapproach for events identification on Twitter extending existing methodologiesof event study. We also propose a robust method for clustering Twitter eventsinto different types based on their shape, which captures the varying dynamicsof information propagation through the social network. We provide empiricalevidence that through events differentiation based on their shape we canclearly identify types of Twitter events that have a more significant power topredict spikes in sales than the aggregated Twitter signal.

In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales