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

Abstract

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.

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

“…Another way a platform might seek to learn about its position on a content manifold might be to directly ask users about how much unwanted time they spend on the platform, sometimes termed "regretful use" [4]. For instance, suppose a platform surveyed users to determine how much time they want to spend on the platform, and compare this to the actual amount of time they spend.…”

“…Taken together, these differences imply that we should modify the model such that whether or not system 1 is active at step t depends not on the content consumed at the previous step, but on the properties of the d pieces of content recommended for consumption at step t + 1. 4 As before, we will assume that each piece of content has parameters (p, q, v). Denote the parameters of the d options presented to the user at step t as {(p i , q i , v i,t ) : i ∈ {1, .…”

“…The relationship between behavior and users' true preferences has been studied both theoretically and empirically under a variety of names, including behaviorism [5], digital addiction [3], regretful or problematic use [4,23], value measurement [20,17], and value-alignment [25]. In practice, some have sought to address this issue by developing tools to help users practice self-control (e.g., Hiniker et al [11]; see Cho et al [4] for more examples).…”

GreaseVision: Rewriting the Rules of the Interface

“…Low-code development platforms have been defined, according to practitioners, to be ( User studies have shown that users can self-identify malevolent harms and habits upon self-reflection and develop desires to intervene against them [9,60]. Not only do end-users have a desire or interest in self-reflection, but there is indication that end-users have a willingness to act.…”

“…An intersecting data source that enables both end-user self-reflection [9,60] and interface re-rendering via overlay [12] is the screen history or interface interaction history (denoted as screenome). Specifically, we can orchestrate a loop that receives input from users and generates outputs for users.…”