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New time-based model to identify the influential users in online social networks

New time-based model to identify the influential users in online social networks Users are the key players in an online social network (OSN), so the behavior of the OSN is strongly related to their behavior. User weight refers to the influence of the users on the OSN. The purpose of this paper is to propose a method to identify the user weight based on a new metric for defining the time intervals.Design/methodology/approachThe behavior of an OSN changes over time, thus the user weight in the OSN is different in each time frame. Therefore, a good metric for estimating the user weight in an OSN depends on the accuracy of the metric used to define the time interval. New metric for defining the time intervals is based on the standard deviation and identifies that the user weight is based on a simple exponential smoothing model.FindingsThe results show that the proposed method covers the maximum behavioral changes of the OSN and is able to identify the influential users in the OSN more accurately than existing methods.Research limitations/implicationsIn event detection, when a terrorist attack occurs as an event, knowing the influential users help us to know the leader of the attack. Knowing the influential user in each time interval based on this study can help us to detect communities which formed around these people. Finally, in marketing, this issue helps us to have a targeted advertising.Practical implicationsUser effect is a significant issue in many OSN domain problems, such as community detection, event detection and recommender systems.Originality/valuePrevious studies do not give priority to the recent time intervals in identifying the relative importance of users. Thus, defining a metric to compute a time interval that covers the maximum changes in the network is a major shortcoming of earlier studies. Some experiments were conducted on six different data sets to test the performance of the proposed model in terms of the computed time intervals and user weights. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Data Technologies and Applications Emerald Publishing

New time-based model to identify the influential users in online social networks

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References (20)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2514-9288
DOI
10.1108/dta-08-2017-0056
Publisher site
See Article on Publisher Site

Abstract

Users are the key players in an online social network (OSN), so the behavior of the OSN is strongly related to their behavior. User weight refers to the influence of the users on the OSN. The purpose of this paper is to propose a method to identify the user weight based on a new metric for defining the time intervals.Design/methodology/approachThe behavior of an OSN changes over time, thus the user weight in the OSN is different in each time frame. Therefore, a good metric for estimating the user weight in an OSN depends on the accuracy of the metric used to define the time interval. New metric for defining the time intervals is based on the standard deviation and identifies that the user weight is based on a simple exponential smoothing model.FindingsThe results show that the proposed method covers the maximum behavioral changes of the OSN and is able to identify the influential users in the OSN more accurately than existing methods.Research limitations/implicationsIn event detection, when a terrorist attack occurs as an event, knowing the influential users help us to know the leader of the attack. Knowing the influential user in each time interval based on this study can help us to detect communities which formed around these people. Finally, in marketing, this issue helps us to have a targeted advertising.Practical implicationsUser effect is a significant issue in many OSN domain problems, such as community detection, event detection and recommender systems.Originality/valuePrevious studies do not give priority to the recent time intervals in identifying the relative importance of users. Thus, defining a metric to compute a time interval that covers the maximum changes in the network is a major shortcoming of earlier studies. Some experiments were conducted on six different data sets to test the performance of the proposed model in terms of the computed time intervals and user weights.

Journal

Data Technologies and Applications – Emerald Publishing

Published: Mar 22, 2018

Keywords: Influential users; OSN; Simple exponential smoothing; Standard deviation; Time interval; User weight

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