Deceptive/Manipulative Patterns (DMP) are interface designs, also known as “dark patterns,” that manipulate user behavior. While considerable attention has been paid to their ethical and legal implications, empirical evidence about their real-world effects remains diffuse. This review synthesizes up-to-date experimental studies, focusing on works that quantify how (or whether) DMPs influence users. We also aggregate findings on interventions aimed at reducing DMP effects. Our synthesis highlights the experimental agreement that DMPs do significantly alter user behavior (with large variance in effect size) and that external interventions have been mostly unsuccessful in mitigating their effects. Lastly, we show that significant correlations between DMP effects and personal characteristics (e.g., age or political affiliation) are uncommon, indicating DMPs similarly affected nearly all populations tested. By summarizing the experimental evidence, we clarify the effects of DMPs, highlight gaps and tensions in the existing experimental literature, and help inform ongoing research and policy directions.

To meet the demands of content moderation, online platforms have resorted to automated systems. Newer forms of real-time engagement (\textit{e.g.}, users commenting on live streams) on platforms like Twitch exert additional pressures on the latency expected of such moderation systems. Despite their prevalence, relatively little is known about the effectiveness of these systems. In this paper, we conduct an audit of Twitch’s automated moderation tool (\texttt{AutoMod}) to investigate its effectiveness in flagging hateful content. For our audit, we create streaming accounts to act as siloed test beds, and interface with the live chat using Twitch’s APIs to send over 107,000 comments collated from 4 datasets. We measure \texttt{AutoMod}‘s accuracy in flagging blatantly hateful content containing misogyny, racism, ableism and homophobia. Our experiments reveal that a large fraction of hateful messages, up to 94% on some datasets, \text{\textit{bypass moderation}}. Contextual addition of slurs to these messages results in 100% removal, revealing \texttt{AutoMod}‘s reliance on slurs as a hate signal. We also find that contrary to Twitch’s community guidelines, \texttt{AutoMod} blocks up to 89.5% of benign examples that use sensitive words in pedagogical or empowering contexts. Overall, our audit points to large gaps in \texttt{AutoMod}‘s capabilities and underscores the importance for such systems to understand context effectively.

Prior work on dark patterns, or manipulative online interfaces, suggests they have potentially detrimental effects on user autonomy. Dark pattern features, like those designed for attention capture, can potentially extend platform sessions beyond what users would have otherwise intended. Existing research, however, has not formally measured the quantitative effects of these features on user engagement in subscription video-on-demand platforms (SVODs). In this work, we conducted an experimental study with 76 Netflix users in the US to analyze the impact of a specific attention capture feature, autoplay, on key viewing metrics. We found that disabling autoplay on Netflix significantly reduced key content consumption aggregates, including average daily watching and average session length, partly filling the evidentiary gap regarding the empirical effects of dark pattern interfaces. We paired the experimental analysis with users' perceptions of autoplay and their viewing behaviors, finding that participants were split on whether the effects of autoplay outweigh its benefits, albeit without knowledge of the study findings. Our findings strengthen the broader argument that manipulative interface designs can and do affect users in potentially damaging ways, highlighting the continued need for considering user well-being and varied preferences in interface design.

Moderating user-generated content on online platforms is crucial for balancing user safety and freedom of speech. Particularly in the United States, platforms are not subject to legal constraints prescribing permissible content. Each platform has thus developed bespoke content moderation policies, but there is little work towards a comparative understanding of these policies across platforms and topics. This paper presents the first systematic study of these policies from the 43 largest online platforms hosting user-generated content, focusing on policies around copyright infringement, harmful speech, and misleading content. We build a custom web-scraper to obtain policy text and develop a unified annotation scheme to analyze the text for the presence of critical components. We find significant structural and compositional variation in policies across topics and platforms, with some variation attributable to disparate legal groundings. We lay the groundwork for future studies of ever-evolving content moderation policies and their impact on users.

Users often turn to subscription video on demand (SVOD) platforms for entertainment. However, these platforms sometimes employ manipulative tactics that undermine a user's sense of agency over time and content choice to increase their share of a user's attention. Prior research has investigated how interface designs affect a user's sense of agency on social media and YouTube. For example, YouTube's autoplay left users feeling like they had less control over their time. We extend this work by investigating the design elements of Netflix, the most used SVOD, for the impact they have on users' senses of agency. We conducted interviews with 20 participants that used Netflix regularly, asking about their experiences and perceptions of features in the Netflix platform design that may affect their sense of agency. We found that a user's sense of agency was at odds with the platform's design. Users, who were often seeking entertainment for mood management, were met with features that encouraged watching more than they originally planned to and watching content they may not otherwise watch. We discuss design recommendations that Netflix could employ to reaffirm their users' agency.

Social media users may wish to delete their accounts, but it is unclear if this process is easy to complete or if users understand what happens to their account data after deletion. Furthermore, since platforms profit from users' data and activity, they have incentives to maintain active users, possibly affecting what account deletion options are offered. To investigate these issues, we conducted a two-part study. In Study Part 1, we created and deleted accounts on the top 20 social media platforms in the United States and performed an analysis of 490 deletion-related screens across these platforms. In Study Part 2, informed by our interface analysis, we surveyed 200 social media users to understand how users perceive and experience social media account deletion. From these studies, we have four main findings. First, account deletion options vary considerably across platforms and the language used to describe these options is not always clear. Most platforms offer account deletion on desktop browsers but not all allow account deletion from mobile apps or browsers. Second, we found evidence of several dark patterns present in the account deletion interfaces and platform policies. Third, most participants had tried to delete at least one social media account, yet over one-third of deletion attempts were never completed. Fourth, users mostly agreed that they did not want platforms to have access to deleted account data. Based on these results, we recommend that platforms improve the terminology used in account deletion interfaces so the outcomes of account deletion are more clear to users. Additionally, we recommend that platforms allow users to delete their social media accounts from any device they use to access the platform. Finally, future work is needed to assess how users are affected by account deletion related dark patterns.

Abstract Once exotic, computational accelerators are now commonly available in many computing systems. Graphics processing units (GPUs) are perhaps the most frequently encountered computational accelerators. Recent work has shown that GPUs are beneficial when analyzing massive data sets. Specifically related to this study, it has been demonstrated that GPUs can significantly reduce the query processing time of database bitmap index queries. Bitmap indices are typically used for large, read-only data sets and are often compressed using some form of hybrid run-length compression. In this paper, we present three GPU algorithm enhancement strategies for executing queries of bitmap indices compressed using word aligned hybrid compression: (1) data structure reuse (2) metadata creation with various type alignment and (3) a preallocated memory pool. The data structure reuse greatly reduces the number of costly memory system calls. The use of metadata exploits the immutable nature of bitmaps to pre-calculate and store necessary intermediate processing results. This metadata reduces the number of required query-time processing steps. Preallocating a memory pool can reduce or entirely remove the overhead of memory operations during query processing. Our empirical study showed that performing a combination of these strategies can achieve 32.4 $$\times$$ × to 98.7 $$\times$$ × speedup over the current state-of-the-art implementation. Our study also showed that by using our enhancements, a common gaming GPU can achieve a $$15.0\times$$ 15.0 × speedup over a more expensive high-end CPU.