Engagement in Motion: Exploring Short Term Dynamics in Page-Level Social Media Metrics

Lucas, Benjamin; Arefin, Ahmed Shamsul; Vries, Natalie Jane de; Berretta, Regina; Carlson, Jamie; Moscato, Pablo

Abstract

Using page-level metrics of a randomly selected group of 15,625 among the top 100,000 Facebook check-in locations which rank high in terms of customer engagement, we explore if the short-term dynamical information on these metrics could deliver, via a clustering approach, some new insights for marketing decision making. Using a highly-scalable clustering algorithm, statistical methods, and combinatorial optimization metaheuristics based on memetic algorithms, we have observed that some pages naturally cluster with others that share the same user-defined category. Our results highlight the need of suggesting other further " meta-categories " that encompass several user-reported categories for pages and that a priori geographical segmentation might be necessary to investigate more relevant patterns that take into account seasonal variability of behaviours and physical proximity.

Citation

Lucas, B., Arefin, A. S., Vries, N. J. D., Berretta, R., Carlson, J., & Moscato, P. (2015). Engagement in Motion: Exploring Short Term Dynamics in Page-Level Social Media Metrics. In 2014 IEEE Fourth International Conference on Big Data and Cloud Computing. https://doi.org/10.1109/bdcloud.2014.56

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2014 IEEE International Conference on Big Data and Cloud Computing (BdCloud)
• Start Date: Dec 3, 2014
• End Date: Dec 5, 2014
• Acceptance Date: Dec 3, 2014
• Online Publication Date: Dec 3, 2014
• Publication Date: Feb 9, 2015
• Deposit Date: Mar 5, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing
• ISBN: 9781479967193
• DOI: https://doi.org/10.1109/bdcloud.2014.56
• Keywords: Page-level metrics; combinatorial optimisation; marketing decision making; memetic algorithms
• Public URL: https://nottingham-repository.worktribe.com/output/1608262
• Publisher URL: https://ieeexplore.ieee.org/document/7034813