Papers

Playing ``Google's Game'': How Educational YouTubers Manage Tensions Between Education and Monetization

YouTube has become an important part of the educational ecosystem, with millions of viewers seeking informative videos and help with coursework. Educational YouTubers create this content, often balancing pedagogical rigor and entertainment value. However, creators need not only to promote their content to find viewers, but also to monetize. In this study, we explore the tensions educational YouTubers face when making monetized educational content. We conduct a qualitative interview study with 12 popular educational YouTubers about their monetization strategies, perceptions of YouTube's algorithmic promotion of their content, and conception of their audience. We find that educational YouTubers are largely driven by a desire to share free and high-quality educational content, and that common monetization strategies like sponsorships and clickbait sometimes interfere with this mission. We describe the careful strategies our participants use to maintain educational integrity while making a living on an algorithmically-driven platform. We then use these findings to draw parallels between YouTubers' challenges with monetizing educational content and the history of educational public broadcast in the United States, which has followed a similar trajectory. In closing, we offer several recommendations for supporting educational YouTubers in creating the high-quality, publicly accessible educational content that is appreciated by a worldwide audience.

Promises, Promises: Understanding Claims Made in Social Robot Consumer Experiences

Social robots are a class of emerging smart consumer electronics devices that promise sophisticated experiences featuring emotive capabilities, artificial intelligence, conversational interaction, and more. With unique risk factors like emotional attachment, little is known on how social robots communicate these promises to consumers and whether they adequately deliver upon them within their overall product experiences prior to and during user interaction. Animated by a consumer protection lens, this paper systematically investigates manufacturer claims made for four commercially available social robots, evaluating these claims against the provided user experience and consumer reviews. We find that social robots vary widely in the manner and extent to which they communicate intelligent features and the supposed benefits of these features, while consumer perspectives similarly include a wide range of perceptions on robot and AI performance, capabilities, and product frustrations.We conclude by discussing social robots' unique characteristics and propensities for consumer risk, and consider implications for key stakeholders like regulators, developers, and researchers of social robots.

Self-Disclosure in Social Virtual Reality: The Influence of Information Management Dynamics, Social Presence, and Privacy Concerns

Social virtual reality (SVR) aims to recreate embodied social experiences similar to those offline. However, concerns about privacy and safety have hindered its widespread adoption. This study examines how information disclosure and perceived control over information in SVR are influenced by 1) boundary permeability (e.g., interruptions from an unknown external user) and 2) identifiability of one’s conversation partner (e.g., access to their offline profile). We also explore how different social presence perceptions and privacy concerns may mediate these relationships. Comparing the experiences of participants (n = 94) randomly assigned to four different mock interview scenarios, we find the perceived actorhood of one’s conversation partner mediated the positive relationship between offline profile access and disclosure. Additionally, more permeable environmental boundaries led to significantly lower levels of disclosure. Qualitative responses emphasized SVR’s limitations in saliently conveying nonverbal expressions. Implications for future research and the design of SVR as a viable communication medium are discussed.

Profiling the Offline and Online Risk Experiences of Youth to Develop Targeted Interventions for Online Safety

We conducted a study with 173 adolescents (ages 13-21), who self-reported their offline and online risk experiences and uploaded their Instagram data to our study website to flag private conversations as unsafe. Risk profiles were first created based on the survey data and then compared with the risk-flagged social media data. Five risk profiles emerged: Low Risks (51% of the participants), Medium Risks (29%), Increased Sexting (8%), Increased Self-Harm (8%), and High Risk Perpetration (4%). Overall, the profiles correlated well with the social media data with the highest level of risk occurring in the three smallest profiles. Youth who experienced increased sexting and self-harm frequently reported engaging in unsafe sexual conversations. Meanwhile, high risk perpetration was characterized by increased violence, threats, and sales/promotion of illegal activities. A key insight from our study was that offline risk behavior sometimes manifested differently in online contexts (i.e., offline self-harm as risky online sexual interactions). Our findings highlight the need for targeted risk prevention strategies for youth online safety.

The Sociotechnical Stack: Opportunities for Social Computing Research in Non-Consensual Intimate Media

Non-consensual intimate media (NCIM) involves sharing intimate content without the depicted person's consent, including ``revenge porn'' and sexually explicit deepfakes. While NCIM has received attention in legal, psychological, and communication fields over the past decade, it is not sufficiently addressed in computing scholarship. This paper addresses this gap by linking NCIM harms to the specific technological components that facilitate them. We introduce the sociotechnical stack, a conceptual framework designed to map the technical stack to its corresponding social impacts. The sociotechnical stack allows us to analyze sociotechnical problems like NCIM, and points toward opportunities for computing research. We propose a research roadmap for computing and social computing communities to deter NCIM perpetration and support victim-survivors through building and rebuilding technologies.

ADA-SHARK: A Shark Detection Framework Employing Underwater Cameras and Domain Adversarial Neural Nets

StructCurves: Interlocking Block-Based Line Structures

We present a new class of curved block-based line structures whose component chains are flexible when separated, and provably rigid when assembled together into an interlocking double chain. The joints are inspired by traditional zippers, where a binding fabric or mesh connects individual teeth. Unlike traditional zippers, the joint design produces a rigid interlock with programmable curvature. This allows fairly strong curved structures to be built out of easily stored flexible chains. In this paper, we introduce a pipeline for generating these curved structures using a novel block design template based on revolute joints. Mesh embedded in these structures maintains block spacing and assembly order. We evaluate the rigidity of the curved structures through mechanical performance testing and demonstrate several applications.

What's in a cable? Abstracting Knitting Design Elements with Blended Raster/Vector Primitives

In chart-based programming environments for machine knitting, patterns are specified at a low level by placing operations on a grid. This highly manual workflow makes it challenging to iterate on design elements such as cables, colorwork, and texture. While vector-based abstractions for knitting design elements may facilitate higher-level manipulation, they often include interdependencies which require stitch-level reconciliation. To address this, we contribute a new way of specifying knits with blended vector and raster primitives. Our abstraction supports the design of interdependent elements like colorwork and texture. We have implemented our blended raster/vector specification in a direct manipulation design tool where primitives are layered and rasterized, allowing for simulation of the resulting knit structure and generation of machine instructions. Through examples, we show how our approach enables higher-level manipulation of various knitting techniques, including intarsia colorwork, short rows, and cables. Specifically, we show how our tool supports the design of complex patterns including origami pleat patterns and capacitive sensor patches.

A Canary in the AI Coal Mine: American Jews May Be Disproportionately Harmed by Intellectual Property Dispossession in Large Language Model Training

Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their consent. The degree of over-representation ranges from around 2x to around 6.5x. Given that LLMs may substitute for the paid labor of those who produced their training data, they have the potential to cause even more substantial and disproportionate economic harm to Jewish Americans in the coming years. This paper focuses on Jewish Americans as a case study, but it is probable that other minority communities (e.g., Asian Americans, Hindu Americans) may be similarly affected and, most importantly, the results should likely be interpreted as a ``canary in the coal mine'' that highlights deep structural concerns about the current LLM paradigm whose harms could soon affect nearly everyone. We discuss the implications of these results for the policymakers thinking about how to regulate LLMs as well as for those in the AI field who are working to advance LLMs. Our findings stress the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms.

Systemization of Knowledge (SoK): Creating a Research Agenda for Human-Centered Real-Time Risk Detection on Social Media Platforms

Accurate real-time risk identification is vital to protecting social media users from online harm, which has driven research towards advancements in machine learning (ML). While strides have been made regarding the computational facets of algorithms for "real-time'' risk detection, such research has not yet evaluated these advancements through a human-centered lens. To this end, we conducted a systematic literature review of 53 peer-reviewed articles on real-time risk detection on social media. Real-time detection was mainly operationalized as "early'' detection after-the-fact based on pre-defined chunks of data and evaluated based on standard performance metrics, such as timeliness. We identified several human-centered opportunities for advancing current algorithms, such as integrating human insight in feature selection, algorithms' improvement considering human behavior, and utilizing human evaluations. This work serves as a critical call-to-action for the HCI and ML communities to work together to protect social media users before, during, and after exposure to risks.

KnitScape: Computational Design and Yarn-Level Simulation of Slip and Tuck Colorwork Knitting Patterns

Slipped and tucked stitches introduce small areas of deformation that compound and result in emergent textures on knitted fabrics. When used together with color changes and ladders, these can also produce dramatic colorwork and openwork effects. However, designing slip and tuck colorwork patterns is challenging due to the complex interactions between operations, yarns, and deformations. We present KnitScape, a browser-based tool for design and simulation of stitch patterns for knitting. KnitScape provides a design interface to specify 1) operation repeats, 2) color changes, and 3) needle positions. These inputs are used to build a graph of yarn topology and run a yarn-level spring simulation. This enables visualization of the deformation that arises from slip and tuck operations. Through its design tool and simulation, KnitScape enables rapid exploration of a complex colorwork design space. We demonstrate KnitScape with a series of example swatches.

"Delete it and Move On": Digital Management of Shared Sexual Content after a Breakup

Sexting is a common and healthy behavior in romantic and sexual relationships. However, not every relationship lasts. When a relationship ends, the fate of sexual content that was previously shared can be a source of discomfort, anxiety, or fear for individuals who may no longer trust their former partners. In extreme cases, intimate content may be leaked or misused by its recipient. To investigate opportunities for building safer sexting tools with breakups in mind, we conducted a survey with 310 U.S. adults who have sexted in the last year. We asked about their sexting practices, communication practices within their relationship about sexting, and preferences for their own sexting content after a breakup. We find that most people save sexts in some form, either actively (e.g., via screenshots) or passively (e.g., in chat history). There is no consensus around what one should do with an ex's content: although most (55%) want their content to be deleted at the end of a relationship, many others don't care (25%) or even hope their ex keeps the material (11%). However, most have never spoken to their partner about this preference. We end with design recommendations that support sexting while keeping the entire relationship lifecycle in mind.

PrISM-Tracker: A Framework for Multimodal Procedure Tracking Using Wearable Sensors and State Transition Information with User-Driven Handling of Errors and Uncertainty

A user often needs training and guidance while performing several daily life procedures, e.g., cooking, setting up a new appliance, or doing a COVID test. Watch-based human activity recognition (HAR) can track users' actions during these procedures. However, out of the box, state-of-the-art HAR struggles from noisy data and less-expressive actions that are often part of daily life tasks. This paper proposes PrISM-Tracker, a procedure-tracking framework that augments existing HAR models with (1) graph-based procedure representation and (2) a user-interaction module to handle model uncertainty. Specifically, PrISM-Tracker extends a Viterbi algorithm to update state probabilities based on time-series HAR outputs by leveraging the graph representation that embeds time information as prior. Moreover, the model identifies moments or classes of uncertainty and asks the user for guidance to improve tracking accuracy. We tested PrISM-Tracker in two procedures: latte-making in an engineering lab study and wound care for skin cancer patients at a clinic. The results showed the effectiveness of the proposed algorithm utilizing transition graphs in tracking steps and the efficacy of using simulated human input to enhance performance. This work is the first step toward human-in-the-loop intelligent systems for guiding users while performing new and complicated procedural tasks. https://dl.acm.org/doi/10.1145/3569504

ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Data

Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose ODSearch, an On-device Search framework equipped with a natural language interface for mobile and wearable devices. To implement search, ODSearch employs compression and Bloom filter, it provides near real-time search query responses without network dependency. In particular, the Bloom filter reduces the temporal scope of the search and compression reduces the size of the data to be searched. Our experiments were conducted on a mobile phone and smartwatch. We compared ODSearch with current state-of-the-art search mechanisms, and it outperformed them on average by 53 times in execution time, 26 times in energy usage, and 2.3% in memory utilization. https://dl.acm.org/doi/10.1145/3569488

Getting Meta: A Multimodal Approach for Detecting Unsafe Conversations within Instagram Direct Messages of Youth

Instagram, one of the most popular social media platforms among youth, has recently come under scrutiny for potentially being harmful to the safety and well-being of our younger generations. Automated approaches for risk detection may be one way to help mitigate some of these risks if such algorithms are both accurate and contextual to the types of online harms youth face on social media platforms. However, the imminent switch by Instagram to end-to-end encryption will limit the type of data that will be available to the platform to detect and mitigate such risks. In this paper, we investigate which indicators are most helpful in automatically detecting risk in Instagram private conversations, with an eye on high-level metadata, which will still be available in the scenario of end-to-end encryption. Toward this end, we collected Instagram data from 172 youth (ages 13-21) and asked them to identify private message conversations that made them feel uncomfortable or unsafe. Our participants risk-flagged 28,725 conversations that contained 4,181,970 direct messages, including textual posts and images. Based on this rich and multimodal dataset, we tested multiple feature sets (metadata, linguistic cues, and image features) and trained classifiers to detect risky conversations. Overall, we found that the metadata features (e.g., conversation length, a proxy for participant engagement) were the best predictors of risky conversations. However, for distinguishing between risk types, the different linguistic and media cues were the best predictors. Based on our findings, we provide design implications for AI risk detection systems in the presence of end-to-end encryption. More broadly, our work contributes to the literature on adolescent online safety by moving toward more robust solutions for risk detection that directly takes into account the lived risk experiences of youth.

Sliding into My DMs: Detecting Uncomfortable or Unsafe Sexual Risk Experiences within Instagram Direct Messages Grounded in the Perspective of Youth

We collected Instagram data from 150 adolescents (ages 13-21) that included 15,547 private message conversations of which 326 conversations were flagged as sexually risky by participants. Based on this data, we leveraged a human-centered machine learning approach to create sexual risk detection classifiers for youth social media conversations. Our Convolutional Neural Network (CNN) and Random Forest models outperformed in identifying sexual risks at the conversation-level (AUC=0.88), and CNN outperformed at the message-level (AUC=0.85). We also trained classifiers to detect the severity risk level (i.e., safe, low, medium-high) of a given message with CNN outperforming other models (AUC=0.88). A feature analysis yielded deeper insights into patterns found within sexually safe versus unsafe conversations. We found that contextual features (e.g., age, gender, and relationship type) and Linguistic Inquiry and Word Count (LIWC) contributed the most for accurately detecting sexual conversations that made youth feel uncomfortable or unsafe. Our analysis provides insights into the important factors and contextual features that enhance automated detection of sexual risks within youths' private conversations. As such, we make valuable contributions to the computational risk detection and adolescent online safety literature through our human-centered approach of collecting and ground truth coding private social media conversations of youth for the purpose of risk classification.

Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter

While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media, and reveals some characteristics of users who post COVID-19 misinformation images.

"Everyone is Covered": Exploring the Role of Online Interactions in Facilitating Connection and Social Support in Black Churches

Faith institutions provide social support and community care for many in the United States (U.S.). Notably, churches with predominantly Black populations have served as a site for social change and care provision, historically and in contemporary society. However, the pandemic has emphasised how localising these care networks in physical spaces can limit access to social support. Information and communication technologies offer opportunities for expanding access to care in these communities. However, integrating care networks into online contexts remains a challenge for many churches, and the potential for technology to expand these networks is not well understood. Through interviews and focus groups with nine church members, we explore how hybrid faith communities that bridge offline and online contexts can enable social support and care provision. Our findings highlight care network structures in Black churches, barriers to embedding these networks online and strategies for building more seamless hybrid support systems.

``Nudes? Shouldn't I charge for these?'': Motivations of New Sexual Content Creators on OnlyFans

With over 1.5 million content creators, OnlyFans is one of the fastest growing subscription-based social media platforms. The platform is primarily associated with sexual content. Thus, OnlyFans creators are uniquely positioned at the intersection of professional social media content creation and sex work. While the motivations of experienced sex workers to adopt OnlyFans have been studied, in this work we seek to understand the motivations of creators who had not previously done sex work. Through a qualitative interview study of 22 U.S.-based OnlyFans creators, we find that beyond the typical motivations for pursuing gig work (e.g., flexibility, autonomy), our participants were motivated by three key factors: (1) societal visibility and mainstream acceptance of OnlyFans; (2) platform design and affordances such as boundary-setting with clients, privacy from the public, and content archives; and (3) the pandemic, as OnlyFans provided an enormous opportunity to overcome lockdown-related issues.

Understanding Dark Patterns in Home IoT Devices

Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits. Speakers, doorbells, and camera devices contain the most dark patterns, with manufacturers of such devices (Amazon and Google) having the most dark patterns compared to other vendors. We investigate how this distribution impacts the potential for consumer exposure to dark patterns, discuss broader implications for key stakeholders like designers and regulators, and identify opportunities for future dark patterns study.