Hao-Ping (Hank) Lee

Social media websites thrive on user engagement by employing Attention Capture Damaging Patterns (ACDPs), e.g., infinite scroll, that prey on cognitive vulnerabilities to distract users. Prior work has taxonomized these ACDPs, but we have yet to measure how the presence of ACDPs impacts perceived distraction nor how mechanisms that suppress ACDPs reduce distraction. We conducted a two-week, mixed-methods field study with 29 participants to model how people get distracted when browsing social media websites, and how ACDPs might play a role. In the first week of the study, we sample participants’ in-situ perceptions of distraction, subjective perceptions of the browsing session (e.g., satisfaction), and the presence/absence of ACDPs. Participants reported feeling distracted 28% of the time, and that subjective perceptions and some ACDPs (e.g., notifications) highly correlated with when they felt distracted. In the second week of the study, participants were given access to Purpose Mode — a browser extension that allows users to “toggle off” ACDPs. Participants reported feeling distracted only 7% of the time and spent 21 fewer daily minutes browsing these websites. We discovered that Purpose Mode empowered users to feel more in control over their social media browsing and made participants feel less irritated and frustrated.

The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users’ perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users’ erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users’ ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigm shifts to protect the privacy of LLM-based CA users.

Research shows that smartphone users often attend to phone notifications that are in the middle of the notification list. This suggests a mismatch between the display order and the users' attendance order on the notifications. Yet, we know little about how users would like their notifications to be sorted and presented. This paper presents the preliminary results of a mixed-methods study of the difference between smartphone users' attendance order and their desired display order of smartphone notifications. Our preliminary results show that a mismatch between attendance order and desired display order existed in nearly half of cases. Specifically, many users desired certain categories of notifications to be placed higher in their notification drawers than their actual notification-attendance behaviors would tend to suggest. Additionally, while our participants felt that some notifications have low-attractiveness senders or content, such as shopping-related ones, they would want the system to give them a higher priority.