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....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/1/119 |
| _version_ | 1811184382289903616 |
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| author | Thanh Trinh Dingming Wu Joshua Zhexue Huang Muhammad Azhar |
| author_facet | Thanh Trinh Dingming Wu Joshua Zhexue Huang Muhammad Azhar |
| author_sort | Thanh Trinh |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T13:11:20Z |
| format | Article |
| id | doaj.art-2b8dfaa29d4d4afdba43671de748275f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:11:20Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-2b8dfaa29d4d4afdba43671de748275f2022-12-22T04:22:34ZengMDPI AGEntropy1099-43002020-01-0122111910.3390/e22010119e22010119Activeness and Loyalty Analysis in Event-Based Social NetworksThanh Trinh0Dingming Wu1Joshua Zhexue Huang2Muhammad Azhar3College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaEvent-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.https://www.mdpi.com/1099-4300/22/1/119social networksebsnsactivenessloyalty |
| spellingShingle | Thanh Trinh Dingming Wu Joshua Zhexue Huang Muhammad Azhar Activeness and Loyalty Analysis in Event-Based Social Networks Entropy social networks ebsns activeness loyalty |
| 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 | social networks ebsns activeness loyalty |
| url | https://www.mdpi.com/1099-4300/22/1/119 |
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