UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Kolchyna, O; Souza, TTP; Treleaven, PC; Aste, T; (2016) A Framework for Twitter Events Detection, Differentiation and its Application for Retail Brands. In: 2016 Future Technologies Conference (FTC 2016). (pp. pp. 323-331). IEEE Green open access

[thumbnail of Paper 455-A Framework for Twitter Events Detection.pdf]
Preview
Text
Paper 455-A Framework for Twitter Events Detection.pdf - Accepted Version

Download (744kB) | Preview

Abstract

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.

Type: Proceedings paper
Title: A Framework for Twitter Events Detection, Differentiation and its Application for Retail Brands
Event: FTC 2016, Future Technologies Conference, 6-7 December 2016, San Francisco, California, USA
Location: San Francisco, CA
Dates: 06 December 2016 - 07 December 2016
ISBN-13: 9781509041725
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.1109/FTC.2016.7821630
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Twitter, Anomaly detection, clustering, event detection, event study, spikes, social media
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10043050
Downloads since deposit
200Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item