Supporting Serendipitous Discovery and Balanced Analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions

TL;DR

This paper presents Serendyze, a system with interaction-driven metrics and bias-mitigating suggestions that enhances online review exploration, promoting serendipitous discovery and balanced analysis to support better purchase decisions.

Contribution

It introduces novel exploration metrics and a bias mitigation model integrated into Serendyze, improving review exploration and decision confidence in online shopping.

Findings

Exploration metrics increased review coverage and balance.

Bias mitigation suggestions boosted decision confidence.

System encouraged diverse and comprehensive review analysis.

Abstract

In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions -- Exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.