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A two-dimensional framework for profiling online reviewer behavior.

Luisa Stracqualursi1, Patrizia Agati1

  • 1Department of Statistics, University of Bologna, Bologna, Italy.

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|March 25, 2026
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Summary
This summary is machine-generated.

Understanding online reviewer behavior is key. This study introduces a framework using the Reviewer Extremeness Index (REI) and Reviewer Polarity Index (RPI) to profile users, enhancing trust in online reviews.

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Area of Science:

  • Information Science
  • Computational Social Science
  • Behavioral Economics

Background:

  • Consumers often rely on extreme online reviews, leading to potential confusion and distrust.
  • Existing rating systems lack metrics to deeply analyze reviewer behavior patterns.

Purpose of the Study:

  • To introduce a novel two-dimensional framework for profiling reviewer behavior.
  • To quantify reviewer tendencies using the Reviewer Extremeness Index (REI) and Reviewer Polarity Index (RPI).

Main Methods:

  • Developed a framework mapping reviewers onto a 2D plane based on REI and RPI.
  • Identified nine archetypal profiles of reviewer behavior.
  • Applied the framework to analyze three million Amazon book reviews.

Main Results:

  • The framework successfully profiles reviewer behavior based on historical extreme ratings.
  • Demonstrated practical application and scalability in a large-scale e-commerce dataset.
  • Identified distinct reviewer archetypes for nuanced interpretation.

Conclusions:

  • The proposed framework enhances consumer trust by providing contextual reviewer information.
  • Offers online platforms a tool to monitor behavior and detect potential rating manipulation.
  • Contributes to a more reliable and transparent digital marketplace.