Identifying the perceptive users for online social systems

Identifying the perceptive users for online social systems

In this paper, the perceptive user, who could identify the high-quality objects in their initial lifespan, is presented. By tracking the ratings given to the rewarded objects, we present a method to identify the user perceptibility, which is defined as the capability that a user can identify these o...

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Bibliographic Details
Published in:PloS one 2017-07, Vol.12 (7), p.e0178118-e0178118
Main Authors: Liu, Jian-Guo, Liu, Xiao-Lu, Guo, Qiang, Han, Jing-Ti
Format: Article
Language:eng
Subjects:
Analysis
Artificial intelligence
Biology and Life Sciences
Cloud computing
Computer and Information Sciences
Correlation
Datasets
Grammy awards
Humans
Hysteresis
Internet
Interpersonal Relations
Life span
Methods
Models, Psychological
Models, Statistical
On-line systems
Online social networks
Online Systems
Quality
Ratings & rankings
Reputations
Research and Analysis Methods
Social behavior
Social Media
Social perception
Social Sciences
Social Support
Systems science
User behavior
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Summary:In this paper, the perceptive user, who could identify the high-quality objects in their initial lifespan, is presented. By tracking the ratings given to the rewarded objects, we present a method to identify the user perceptibility, which is defined as the capability that a user can identify these objects at their early lifespan. Moreover, we investigate the behavior patterns of the perceptive users from three dimensions: User activity, correlation characteristics of user rating series and user reputation. The experimental results for the empirical networks indicate that high perceptibility users show significantly different behavior patterns with the others: Having larger degree, stronger correlation of rating series and higher reputation. Furthermore, in view of the hysteresis in finding the rewarded objects, we present a general framework for identifying the high perceptibility users based on user behavior patterns. The experimental results show that this work is helpful for deeply understanding the collective behavior patterns for online users.
ISSN:1932-6203
1932-6203