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Measurement of Trustworthiness of the Online Reviews

Dipankar Das

TL;DR

The paper formalizes a framework to quantify the trustworthiness of online reviews in sequential e-commerce choices by introducing a rationality pattern function and the Average Propensity to Choose a Pattern (APCP). It combines past reviewer forecasts with Bayesian updating, incorporating a trustfulness parameter $\nu_t$ and belief dynamics to produce a posterior assessment of review reliability. Through a two-period, four-object example, it derives patterns, constructs joint rationality metrics, and develops a systematic APCP-based ranking to identify trustworthy reviews. The approach aims to reduce information asymmetry, detect manipulative reviews, and inform revenue-related decision-making in digital markets. Although the model relies on several structural assumptions and a limited object set, it provides a rigorous basis for measuring reviewer rationality and integrating it into online-information updating processes.

Abstract

In electronic commerce (e-commerce)markets, a decision-maker faces a sequential choice problem. Third-party intervention is essential in making purchase decisions in this choice process. For instance, while purchasing products/services online, a buyer's choice or behavior is often affected by the overall reviewers' ratings, feedback, etc. Moreover, the reviewer is also a decision-maker. The question that arises is how trustworthy these review reports and ratings are. The trustworthiness of these review reports and ratings is based on whether the reviewer is rational or irrational. Indexing the reviewer's rationality could be a way to quantify a reviewer's rationality, but it needs to communicate the history of their behavior. In this article, the researcher aims to derive a rationality pattern function formally and, thereby, the degree of rationality of the decision-maker or the reviewer in the sequential choice problem in the e-commerce markets. Applying such a rationality pattern function could make quantifying the rational behavior of an agent participating in the digital markets easier. This, in turn, is expected to minimize the information asymmetry within the decision-making process and identify the paid reviewers or manipulative reviews.

Measurement of Trustworthiness of the Online Reviews

TL;DR

The paper formalizes a framework to quantify the trustworthiness of online reviews in sequential e-commerce choices by introducing a rationality pattern function and the Average Propensity to Choose a Pattern (APCP). It combines past reviewer forecasts with Bayesian updating, incorporating a trustfulness parameter and belief dynamics to produce a posterior assessment of review reliability. Through a two-period, four-object example, it derives patterns, constructs joint rationality metrics, and develops a systematic APCP-based ranking to identify trustworthy reviews. The approach aims to reduce information asymmetry, detect manipulative reviews, and inform revenue-related decision-making in digital markets. Although the model relies on several structural assumptions and a limited object set, it provides a rigorous basis for measuring reviewer rationality and integrating it into online-information updating processes.

Abstract

In electronic commerce (e-commerce)markets, a decision-maker faces a sequential choice problem. Third-party intervention is essential in making purchase decisions in this choice process. For instance, while purchasing products/services online, a buyer's choice or behavior is often affected by the overall reviewers' ratings, feedback, etc. Moreover, the reviewer is also a decision-maker. The question that arises is how trustworthy these review reports and ratings are. The trustworthiness of these review reports and ratings is based on whether the reviewer is rational or irrational. Indexing the reviewer's rationality could be a way to quantify a reviewer's rationality, but it needs to communicate the history of their behavior. In this article, the researcher aims to derive a rationality pattern function formally and, thereby, the degree of rationality of the decision-maker or the reviewer in the sequential choice problem in the e-commerce markets. Applying such a rationality pattern function could make quantifying the rational behavior of an agent participating in the digital markets easier. This, in turn, is expected to minimize the information asymmetry within the decision-making process and identify the paid reviewers or manipulative reviews.
Paper Structure (63 sections, 4 theorems, 42 equations, 5 figures, 3 tables, 1 algorithm)

This paper contains 63 sections, 4 theorems, 42 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Literature
  3. Theoretical Motivation
  4. Statement of Intended Contribution
  5. Remarks 1
  6. Effect of Bias Forecast and Bayesian Approach: A Statistical Implication
  7. Customers' belief updating rules: Belief updating with value-based reviews
  8. Objective of the Study
  9. Model
  10. Proof:
  11. Remarks 2
  12. Remarks 3
  13. Remark 4
  14. The Choice Process
  15. Single Agent and Two Periods Model of the Rationality Pattern
  16. ...and 48 more sections

Key Result

Theorem 1

A two-way consistency of the choice problem exists. The choice would be the same if the decision-maker moves from a large set to a small set and from a small one to a large one. This happens provided the decision-maker can interpret the information correctly for each set.

Figures (5)

  • Figure 1: Review Rating-Example from amazon.in
  • Figure 2: Review Comments-Example from amazon.in
  • Figure 3: Distribution of the Ratings on Amazon.com (A U-shaped curve)
  • Figure 4: Preference Graph
  • Figure 5: Aggregate Preference Graph

Theorems & Definitions (13)

  • Definition 1
  • Theorem 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Lemma 1
  • Proposition 1
  • Lemma 2
  • ...and 3 more