Influence or Deception? Evaluating Social Suggestions with Persuasive Statements for Security and Privacy Settings

要旨

Configuring security and privacy (S&P) settings can be challenging for non-expert users, resulting in excessive dependence on persuasive cues, such as social proofs or expert suggestions. Although such suggestions can promote protective user choices, they can be misused as deceptive patterns that steer users toward less-protective settings. This study examines (1) how source-based suggestions (public vs. experts), when combined with logical persuasive statements, influence decision-making in S&P settings under honest or deceptive conditions and (2) how users evaluate these approaches once deception is revealed. An online experiment with 1,433 U.S. participants utilizing a 2×2×2 factorial design revealed that persuasive statements amplified the effect of social proof- and authority-based cues, which persisted even when promoting less-protective settings. These findings demonstrate the importance of persuasive S&P interfaces that follow transparent and rational design, as well as complementary interventions that foster users' critical assessment and resilience against manipulation.

著者
Ayako A.. Hasegawa
NICT, Tokyo, Japan
Takahiro Kasama
NICT, Tokyo, Japan
Mitsuaki Akiyama
NTT Social Informatics Laboratories, Tokyo, Japan
動画

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Dark Patterns, Deception, and Manipulative Interfaces

P1 - Room 128
7 件の発表
2026-04-16 18:00:00
2026-04-16 19:30:00