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Activeness and Loyalty Analysis in Event-Based Social Networks

Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame....

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Autores principales: Trinh, Thanh, Wu, Dingming, Huang, Joshua Zhexue, Azhar, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516425/
https://www.ncbi.nlm.nih.gov/pubmed/33285894
http://dx.doi.org/10.3390/e22010119
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author Trinh, Thanh
Wu, Dingming
Huang, Joshua Zhexue
Azhar, Muhammad
author_facet Trinh, Thanh
Wu, Dingming
Huang, Joshua Zhexue
Azhar, Muhammad
author_sort Trinh, Thanh
collection PubMed
description Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.
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spelling pubmed-75164252020-11-09 Activeness and Loyalty Analysis in Event-Based Social Networks Trinh, Thanh Wu, Dingming Huang, Joshua Zhexue Azhar, Muhammad Entropy (Basel) Article Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features. MDPI 2020-01-18 /pmc/articles/PMC7516425/ /pubmed/33285894 http://dx.doi.org/10.3390/e22010119 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Trinh, Thanh
Wu, Dingming
Huang, Joshua Zhexue
Azhar, Muhammad
Activeness and Loyalty Analysis in Event-Based Social Networks
title Activeness and Loyalty Analysis in Event-Based Social Networks
title_full Activeness and Loyalty Analysis in Event-Based Social Networks
title_fullStr Activeness and Loyalty Analysis in Event-Based Social Networks
title_full_unstemmed Activeness and Loyalty Analysis in Event-Based Social Networks
title_short Activeness and Loyalty Analysis in Event-Based Social Networks
title_sort activeness and loyalty analysis in event-based social networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516425/
https://www.ncbi.nlm.nih.gov/pubmed/33285894
http://dx.doi.org/10.3390/e22010119
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