research-article

Understanding Event Organization at Scale in Event-Based Social Networks

Authors:

Jason Shuo Zhang,

Qin LvAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 10, Issue 2

Article No.: 16, Pages 1 - 23

https://doi.org/10.1145/3243227

Published: 12 January 2019 Publication History

Abstract

Understanding real-world event participation behavior has been a subject of active research and can offer valuable insights for event-related recommendation and advertisement. The emergence of event-based social networks (EBSNs), which attracts online users to host/attend offline events, has enabled exciting new research in this domain. However, most existing works focus on understanding or predicting individual users’ event participation behavior or recommending events to individual users. Few studies have addressed the problem of event popularity from the event organizer’s point of view.

In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in five major cities around the world. We analyze and model four contextual factors: spatial factor using location convenience, quality, popularity density, and competitiveness; group factor using group member entropy and loyalty; temporal factor using temporal preference and weekly event patterns; and semantic factor using readability, sentiment, part of speech, and text novelty. In addition, we have developed a group-based social influence propagation network to model group-specific influences on events. By combining the COntextual features and Social Influence NEtwork, our integrated prediction framework COSINE can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Detailed evaluations demonstrate that our COSINE framework achieves high accuracy for event popularity prediction in all five cities with diverse cultures and user event behaviors.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 10, Issue 2

Survey Papers and Regular Papers

March 2019

214 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3306498

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2019 ACM.

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Publication History

Published: 12 January 2019

Accepted: 01 July 2018

Revised: 01 June 2018

Received: 01 January 2018

Published in TIST Volume 10, Issue 2

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Zhang HXia YRen YGuan JZhou SWilliams BChen YNeville J(2023)Differentially private nonlinear causal discovery from numerical dataProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i10.26452(12321-12328)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i10.26452
Wang DZhang ZZhao YHuang KKang YZhou JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum LearningProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599351(2233-2242)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599351
Li WHu YJiang CWu SBai QLai E(2023)ABEM: An adaptive agent-based evolutionary approach for influence maximization in dynamic social networksApplied Soft Computing10.1016/j.asoc.2023.110062136(110062)Online publication date: Mar-2023
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