A Framework for Twitter Events Detection, Differentiation and its Application for Retail Brands

We propose a framework for Twitter events detection, differentiation and quantification of their significance for predicting spikes in sales. In previous approaches, the differentiation between Twitter events has mainly been done based on spatial, temporal or topic information. We suggest a novel approach that performs clustering of Twitter events based on their shapes (taking into account growth and relaxation signatures). Our study provides empirical evidence that through events differentiation based on their shape one can clearly identify clusters of Twitter events that contain more information about future sales than the non-clustered Twitter signal. We also propose a method for automatic identification of the optimum event window, solving a task of window selection, which is a common problem in the event study field. The framework described in this paper was tested on a large-scale dataset of 150 million Tweets and sales data of 75 brands, and can be applied to the analysis of time series from other domains

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

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