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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Identifying the perceptive users for online social systems.

Jian-Guo Liu^1,2, Xiao-Lu Liu³, Qiang Guo³

¹Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, PR China.

|July 14, 2017

Summary

This study introduces a method to identify perceptive users who can recognize high-quality items early. Perceptive users exhibit distinct behaviors, including higher activity, stronger rating correlations, and greater reputation.

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Area of Science:

Information Science
Network Analysis
User Behavior Studies

Background:

Identifying users with early insight into item quality is crucial for recommender systems.
Understanding user perceptibility aids in analyzing collective intelligence and online behavior.

Purpose of the Study:

To present a method for identifying 'perceptive users' based on their early identification of high-quality items.
To investigate the behavioral patterns of perceptive users across multiple dimensions.
To develop a framework for recognizing perceptive users using their behavior patterns.

Main Methods:

Tracking user ratings of rewarded objects to define and measure user perceptibility.
Analyzing user activity, rating series correlation, and user reputation.
Developing a general framework for identifying perceptive users based on observed behavior patterns.

Main Results:

Perceptive users demonstrate significantly different behavior patterns compared to non-perceptive users.
Key distinguishing factors include higher network degree, stronger correlation in rating series, and enhanced user reputation.
The proposed framework effectively identifies perceptive users by analyzing their behavioral characteristics.

Conclusions:

This research provides a novel approach to identifying perceptive users in online environments.
Understanding these user behaviors offers deeper insights into collective intelligence and user dynamics.
The findings have implications for improving recommender systems and understanding online user engagement.