In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales
This work addresses the challenge of improving sales forecasting for retail companies by leveraging social media data, though it is incremental in extending existing event study methodologies.
The authors tackled the problem of predicting consumer sales spikes using Twitter sentiment events, finding that differentiating event types by shape identifies more predictive signals than aggregated Twitter data.
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.