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.

Many people are concerned about how their personal data is used for online behavioral advertising (OBA). Ad targeting explanations have been proposed as a way to reduce this concern by improving transparency. However, it is unclear when and why people might want ad targeting explanations. Without this insight, we run the risk of designing explanations that do not address real concerns. To bridge this gap, we conducted a four-week, mixed-methods field study with 60 participants to understand when and why people want targeting explanations for the ads they actually encountered while browsing the web. We found that users wanted explanations for around 30% of the 4,251 ads we asked them about during the study, and that subjective perceptions of how their personal data was collected and shared were highly correlated with when users wanted ad explanations. Often, users wanted these explanations to confirm or deny their own preconceptions about how their data was collected or the motives of advertisers. A key upshot of our work is that one-size-fits-all approaches to ad explanations are likely to fail at addressing people’s lived concerns about ad targeting; instead, more personalized explanations are needed.

In contrast to existing, static audience controls that map poorly onto users' ideal audiences on social networking sites, dynamic audience selection (DAS) controls can make intelligent inferences to help users select their ideal audience given context and content. But does this potential utility outweigh its potential intrusiveness? We surveyed 250 participants to model users' ideal versus their chosen audiences with static controls and found a significant misalignment, suggesting that DAS might provide utility. We then designed a sensitizing prototype that allowed users to select audiences based on personal attributes, content, or context constraints. We evaluated DAS vis-a-vis Facebook's existing audience selection controls through a counterbalanced summative evaluation. We found that DAS's expressiveness, customizability, and scalability made participants feel more confident about the content they shared on Facebook. However, low transparency, distrust in algorithmic inferences, and the emergence of privacy-violating side channels made participants find the prototype unreliable or intrusive. We discuss factors that affected this trade-off between DAS's utility and intrusiveness and synthesize design implications for future audience selection tools.