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
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 |
1. | United States | 11 |
2. | United Kingdom | 7 |
3. | China | 6 |
4. | Italy | 4 |
5. | India | 2 |
6. | Australia | 2 |
7. | Russian Federation | 2 |
8. | Singapore | 1 |
9. | Canada | 1 |
10. | France | 1 |
Archive Staff Only
View Item |