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

TL;DR

This paper investigates how different types of Twitter sentiment events can predict spikes in consumer sales, introducing a new method for classifying events by their shape to enhance prediction accuracy.

Contribution

It proposes a novel approach for identifying and clustering Twitter events based on their shape, improving the prediction of sales spikes from social media data.

Findings

Shape-based event classification improves sales prediction accuracy

Certain Twitter event types are more predictive of sales spikes

Empirical validation on retail data supports the method's effectiveness

Abstract

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.