How the Design of YouTube Influences User Sense of Agency

Lukoff, Kai; Lyngs, Ulrik; Zade, Himanshu; Liao, Jo‐Ling; Choi, James J.; Fan, Kaiyue; Munson, Sean A.; Hiniker, Alexis

doi:10.1145/3411764.3445467

Cited by 82 publications

(44 citation statements)

References 77 publications

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“…A recent study [36] showed how the designs of YouTube's internal mechanisms such as auto-play, recommendations, and advertisements support a sense of agency. It discovered that autoplay and recommendations impair sense of agency while search and playlists support it.…”

Section: Understanding Smartphone Use Behaviorsmentioning

confidence: 99%

Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis

Cho

Choi

Kim

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit on an app. However, today's social media apps offer a mix of quasiessential and addictive features in an app (e.g., Instagram has following feeds, recommended feeds, stories, and direct messaging features), which makes it hard to apply a uniform logic for all uses of an app without a nuanced understanding of feature-level usage behaviors. We study when and why people regret using different features of social media apps on smartphones. We examine regretful feature uses in four smartphone social media apps (Facebook, Instagram, YouTube, and KakaoTalk) by utilizing feature usage logs, ESM surveys on regretful use collected for a week, and retrospective interviews from 29 Android users. In determining whether a feature use is regretful, users considered different types of rewards they obtained from using a certain feature (i.e., social, informational, personal interests, and entertainment) as well as alternative rewards they could have gained had they not used the smartphone (e.g., productivity). Depending on the types of rewards and the way rewards are presented to users, probabilities to regret vary across features of the same app. We highlight three patterns of features with different characteristics that lead to regretful use. First, "following"-based features (e.g., Facebook's News Feed and Instagram's Following Posts and Stories) induce habitual checking and quickly deplete rewards from app use. Second, recommendation-based features situated close to actively used features (e.g., Instagram's Suggested Posts adjacent to Search) cause habitual feature tour and sidetracking from the original intention of app use. Third, recommendation-based features with bite-sized contents (e.g., Facebook's Watch Videos) induce using "just a bit more," making people fall into prolonged use. We discuss implications of our findings for how social media apps and intervention tools can be designed to reduce regretful use and how feature-level usage information can strengthen self-reflection and behavior changes.

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Section: Understanding Smartphone Use Behaviorsmentioning

confidence: 99%

Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis

Cho

Choi

Kim

et al. 2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…Also, the principal usability of extension systems has already been widely demonstrated on the web and in research studies leveraging browser extensions. We plan to conduct user studies for specific use cases of app extensions, for example to increase satisfaction in Facebook or YouTube use, in future research, similar to previous research on the web [45,47].…”

Section: Discussionmentioning

confidence: 99%

“…The harms arising from the current design of the app ecosystem are multi-faceted, growing and widespread. They can include the loss of privacy [11,71], dark patterns [32,51,57], digital distraction [26,[45][46][47], digital inclusion [56,67], discrimination and algorithmic fairness [7,10] and harmful social media content [36]. Addressing these harms is particularly relevant in a world that is recovering from the COVID-19 pandemic, and is spending more time on their mobile devices than ever before [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, this concept might still be helpful for researchers to study harms in mobile devices, and for end-users to overcome such harms in their day-to-day lives. At the moment, research studies often choose studying interventions against harms on desktop rather mobile, because no ready-to-use extension system has so far existed [45,47]. Likewise, end-users have limited means to avoid harms in those apps that they rely on in their daily lives, such as social media, shopping, and dating apps.…”

Section: Introductionmentioning

confidence: 99%

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Imagining, Studying and Realising A Less Harmful App Ecosystem

Kollnig¹,

Datta²,

Shadbolt³

2022

Preprint

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This a preprint of work-in-progress.Desktop browser extensions have long allowed users to improve their experience online and tackle widespread harms on websites. So far, no equivalent solution exists for mobile apps, despite the fact that individuals now spend significantly more time on mobile than on desktop, and arguably face similarly widespread harms.In this work, we investigate mobile app extensions, a previously underexplored concept to study and address digital harms within mobile apps in a decentralised, community-driven way. We analyse challenges to adoption of this approach so far, and present a readyto-use implementation for Android as a result of significant and careful system development. Through a range of case studies, we demonstrate that our implementation can already reduce (though not completely eliminate) a wide range of harms -similarly as browser extensions do on desktops.Our method provides a versatile foundation for a range of followup research into digital harms in mobile apps that has not previously been possible, given that browser extensions have long been a fruitful foundation for research studies on desktops. In other words, our system tries to address the gap of a focus on desktop interventions in previous research. CCS CONCEPTS• Social and professional topics → Government technology policy; • Human-centered computing → Collaborative and social computing design and evaluation methods; Accessibility systems and tools; Ubiquitous and mobile devices.

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“…Our model also relates to questions about how design choices can influence user behavior and welfare, often studied in the HCI community. Examples include the impacts of design on agency [16], persuasive technology [7], dark patterns [10], and behavioral change [24,18,19]. Our hope is that our model can provide some insight into what types of design interventions might help users, when they would be appropriate, and how to measure their impacts.…”

Section: Related Workmentioning

confidence: 99%

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Kleinberg¹,

Mullainathan²,

Raghavan³

2022

Preprint

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Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in the way user models are built. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. The behavioral economics and psychology literatures suggest, for example, that we can choose mindlessly or that we can be too myopic in our choices, behaviors that feel entirely familiar on online platforms.In this work, we develop a model of media consumption where users have inconsistent preferences. We consider an altruistic platform which simply wants to maximize user utility, but only observes behavioral data in the form of the user's engagement. We show how our model of users' preference inconsistencies produces phenomena that are familiar from everyday experience, but difficult to capture in traditional user interaction models. These phenomena include users who have long sessions on a platform but derive very little utility from it, and platform changes that steadily raise user engagement before abruptly causing users to go "cold turkey" and quit. A key ingredient in our model is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. Whether improving engagement improves user welfare depends on the direction of movement in the content manifold: for certain directions of change, increasing engagement makes users less happy, while in other directions on the same manifold, increasing engagement makes users happier. We provide a characterization of the structure of content manifolds for which increasing engagement fails to increase user utility. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media.

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How the Design of YouTube Influences User Sense of Agency

Cited by 82 publications

References 77 publications

Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis

Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis

Imagining, Studying and Realising A Less Harmful App Ecosystem

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

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