Query Smarter, Trust Better? Exploring Search Behaviours for Verifying News Accuracy

2025-04-07

AI Summary

This study investigates how query formulation strategies influence users’ ability to assess news credibility. Using a mixed-methods approach—including analysis of real-world search logs, LLM-simulated query strategy generation, controlled experiments, and interface intervention testing—we identify three key findings: (1) Users frequently rely on source-oriented keywords for superficial verification, inadvertently reinforcing misbelief; (2) Multi-turn, high-quality querying significantly improves detection accuracy, and reformulating weak queries using full-article context substantially enhances SERP relevance and informativeness; (3) We propose a SERP-feedback-driven dynamic query optimization mechanism and demonstrate that a lightweight interface “Boost” intervention—designed to encourage deeper document engagement rather than explicit cues—effectively improves verification-oriented search behavior. This work is the first to systematically uncover the pivotal role of query strategies in misinformation detection, providing empirical foundations for search interface design and information literacy interventions.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Search and Optimization: Evaluation and Analysis
• Application Domains: Misinformation & Fake News

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Mitigating misinformation and disinformation

Abstract

While it is often assumed that searching for information to evaluate misinformation will help identify false claims, recent work suggests that search behaviours can instead reinforce belief in misleading news, particularly when users generate queries using vocabulary from the source articles. Our research explores how different query generation strategies affect news verification and whether the way people search influences the accuracy of their information evaluation. A mixed-methods approach was used, consisting of three parts: (1) an analysis of existing data to understand how search behaviour influences trust in fake news, (2) a simulation of query generation strategies using a Large Language Model (LLM) to assess the impact of different query formulations on search result quality, and (3) a user study to examine how 'Boost' interventions in interface design can guide users to adopt more effective query strategies. The results show that search behaviour significantly affects trust in news, with successful searches involving multiple queries and yielding higher-quality results. Queries inspired by different parts of a news article produced search results of varying quality, and weak initial queries improved when reformulated using full SERP information. Although 'Boost' interventions had limited impact, the study suggests that interface design encouraging users to thoroughly review search results can enhance query formulation. This study highlights the importance of query strategies in evaluating news and proposes that interface design can play a key role in promoting more effective search practices, serving as one component of a broader set of interventions to combat misinformation.

Problem

• How search behaviors influence trust in misleading news
• Impact of query generation strategies on news verification accuracy
• Effectiveness of interface design in improving search query formulation

Innovation

• Analyzed search behavior impact on fake news trust
• Simulated query strategies using Large Language Model
• Tested interface design to improve query formulation