arXiv subjects

Hendrik Heuer

Publications and source records attributed to Hendrik Heuer.

At least 19 recordsLinked to original sources

arXiv2026

Deception by Design: A Temporal Dark Patterns Audit of McDonald's Self-Ordering Kiosk Flow

Self-ordering kiosks (SOKs) are widely deployed in fast food restaurants, transforming food ordering into digitally mediated, self-navigated interactions. While these systems enhance efficiency and average order value, they also create opportunities for manipulative interface design practices known as dark patterns. This paper presents a structured audit of the McDonald's self-ordering kiosk in Germany using the Temporal Analysis of Dark Patterns (TADP) framework. Through a scenario-based walkthrough simulating a time-pressured user, we reconstructed and analyzed 12 interface steps across intra-page, inter-page, and system levels. We identify recurring high-level strategies implemented through meso-level patterns such as adding steps, false hierarchy, bad defaults, hiding information, and pressured selling, and low-level patterns including visual prominence, confirmshaming, scarcity framing, feedforward ambiguity, emotional sensory manipulation, and partitioned pricing. Our findings demonstrate how these patterns accumulate across the interaction flow and may be amplified by the kiosk's linear task structure and physical context. These findings suggest that hybrid physical--digital consumer interfaces warrant closer scrutiny within emerging regulatory discussions on dark patterns.

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arXiv2026

Take the Power Back: Screen-Based Personal Moderation Against Hate Speech on Instagram

Hate speech remains a pressing challenge on social media, where platform moderation often fails to protect targeted users. Personal moderation tools that let users decide how content is filtered can address some of these shortcomings. However, it remains an open question on which screens (e.g., the comments, the reels tab, or the home feed) users want personal moderation and which features they value most. To address these gaps, we conducted a three-wave Delphi study with 40 activists who experienced hate speech. We combined quantitative ratings and rankings with open questions about required features. Participants prioritized personal moderation for conversational and algorithmically curated screens. They valued features allowing for reversibility and oversight across screens, while input-based, content-type specific, and highly automated features are more screen specific. We discuss the importance of personal moderation and offer user-centered design recommendations for personal moderation on Instagram.

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arXiv2026

They Think AI Can Do More Than It Actually Can: Practices, Challenges, & Opportunities of AI-Supported Reporting In Local Journalism

Declining newspaper revenues prompt local newsrooms to adopt automation to maintain efficiency and keep the community informed. However, current research provides a limited understanding of how local journalists work with digital data and which newsroom processes would benefit most from AI-supported (data) reporting. To bridge this gap, we conducted 21 semi-structured interviews with local journalists in Germany. Our study investigates how local journalists use data and AI (RQ1); the challenges they encounter when interacting with data and AI (RQ2); and the self-perceived opportunities of AI-supported reporting systems through the lens of discursive design (RQ3). Our findings reveal that local journalists do not fully leverage AI's potential to support data-related work. Despite local journalists' limited awareness of AI's capabilities, they are willing to use it to process data and discover stories. Finally, we provide recommendations for improving AI-supported reporting in the context of local news, grounded in the journalists' socio-technical perspective and their imagined AI future capabilities.

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arXiv2026

Reflecting on 1,000 Social Media Journeys: Generational Patterns in Platform Transition

Social media has billions of users, but we still do not fully understand why users prefer one platform over another. Establishing new platforms among already popular competitors is difficult. Prior research has richly documented people's experiences within individual platforms, yet situating those experiences within the entirety of a user's social media experience remains challenging. What platforms have people used, and why have they transitioned between them? We collected data from a quota-based sample of 1,000 U.S. participants. We introduce the concept of \emph{Social Media Journeys} to study the entirety of their social media experiences systematically. We identify push and pull factors across the social media landscape. We also show how different generations adopted social media platforms based on personal needs. With this work, we advance HCI by moving towards holistic perspectives when discussing social media technology, offering new insights for platform design, governance, and regulation.

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arXiv2026

When Handwriting Goes Social: Creativity, Anonymity, and Communication in Graphonymous Online Spaces

While most digital communication platforms rely on text, relatively little research has examined how users engage through handwriting and drawing in anonymous, collaborative environments. We introduce Graphonymous Interaction, a form of communication where users interact anonymously via handwriting and drawing. Our study analyzed over 600 canvas pages from the Graphonymous Online Space (GOS) CollaNote and conducted interviews with 20 users. Additionally, we examined 70 minutes of real-time GOS sessions using Conversation Analysis and Multimodal Discourse Analysis. Findings reveal that Graphonymous Interaction fosters artistic expression, intellectual engagement, sharing and supporting, and social connection. Notably, anonymity coexisted with moments of recognition through graphological identification. Distinct conversational strategies also emerged, which allow smoother exchanges and fewer conversational repairs compared to text-based communication. This study contributes to understanding Graphonymous Interaction and Online Spaces, offering insights into designing platforms that support creative and socially engaging forms of communication beyond text.

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arXiv2026

A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs

Large Language Models (LLMs) are increasingly used for mental health support, yet little is known about how people with mental health challenges engage with them, how they evaluate their usefulness, and what design opportunities they envision. We conducted 20 semi-structured interviews with people in the UK who live with mental health conditions and have used LLMs for mental health support. Through reflexive thematic analysis, we found that participants engaged with LLMs in conditional and situational ways: for immediacy, the desire for non-judgement, self-paced disclosure, cognitive reframing, and relational engagement. Simultaneously, participants articulated clear boundaries informed by prior therapeutic experience: LLMs were effective for mild-to-moderate distress but inadequate for crises, trauma, and complex social-emotional situations. We contribute empirical insights into the lived use of LLMs for mental health, highlight boundary-setting as central to their safe role, and propose design and governance directions for embedding them responsibly within care ecosystem.

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arXiv2025

Social Media for Activists: Reimagining Safety, Content Presentation, and Workflows

Social media is central to activists, who use it internally for coordination and externally to reach supporters and the public. To date, the HCI community has not explored activists' perspectives on future social media platforms. In interviews with 14 activists from an environmental and a queer-feminist movement in Germany, we identify activists' needs and feature requests for future social media platforms. The key finding is that on- and offline safety is their main need. Based on this, we make concrete proposals to improve safety measures. Increased control over content presentation and tools to streamline activist workflows are also central to activists. We make concrete design and research recommendations on how social media platforms and the HCI community can contribute to improved safety and content presentation, and how activists themselves can reduce their workload.

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arXiv2025

"This could save us months of work" -- Use Cases of AI and Automation Support in Investigative Journalism

As the capabilities of Large Language Models (LLMs) expand, more researchers are studying their adoption in newsrooms. However, much of the research focus remains broad and does not address the specific technical needs of investigative journalists. Therefore, this paper presents several applied use cases where automation and AI intersect with investigative journalism. We conducted a within-subjects user study with eight investigative journalists. In interviews, we elicited practical use cases using a speculative design approach by having journalists react to a prototype of a system that combines LLMs and Programming-by-Demonstration (PbD) to simplify data collection on numerous websites. Based on user reports, we classified the journalistic processes into data collecting and reporting. Participants indicated they utilize automation to handle repetitive tasks like content monitoring, web scraping, summarization, and preliminary data exploration. Following these insights, we provide guidelines on how investigative journalism can benefit from AI and automation.

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arXiv2025

Social Media Journeys -- Mapping Platform Migration

As people engage with the social media landscape, popular platforms rise and fall. As current research uncovers the experiences people have on various platforms, rarely do we engage with the sociotechnical migration processes when joining and leaving them. In this paper, we asked 32 visitors of a science communication festival to draw out artifacts that we call Social Media Journey Maps about the social media platforms they frequented, and why. By combining qualitative content analysis with a graph representation of Social Media Journeys, we present how social media migration processes are motivated by the interplay of environmental and platform factors. We find that peer-driven popularity, the timing of feature adoption, and personal perceptions of migration causes - such as security - shape individuals' reasoning for migrating between social media platforms. With this work, we aim to pave the way for future social media platforms that foster meaningful and enriching online experiences for users.

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arXiv2025

Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations

Commercial content moderation APIs are marketed as scalable solutions to combat online hate speech. However, the reliance on these APIs risks both silencing legitimate speech, called over-moderation, and failing to protect online platforms from harmful speech, known as under-moderation. To assess such risks, this paper introduces a framework for auditing black-box NLP systems. Using the framework, we systematically evaluate five widely used commercial content moderation APIs. Analyzing five million queries based on four datasets, we find that APIs frequently rely on group identity terms, such as ``black'', to predict hate speech. While OpenAI's and Amazon's services perform slightly better, all providers under-moderate implicit hate speech, which uses codified messages, especially against LGBTQIA+ individuals. Simultaneously, they over-moderate counter-speech, reclaimed slurs and content related to Black, LGBTQIA+, Jewish, and Muslim people. We recommend that API providers offer better guidance on API implementation and threshold setting and more transparency on their APIs' limitations. Warning: This paper contains offensive and hateful terms and concepts. We have chosen to reproduce these terms for reasons of transparency.

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arXiv2025

The Phase Model of Misinformation Interventions

Misinformation is a challenging problem. This paper provides the first systematic interdisciplinary investigation of technical and non-technical interventions against misinformation. It combines interviews and a survey to understand which interventions are accepted across academic disciplines and approved by misinformation experts. Four interventions are supported by more than two in three misinformation experts: promoting media literacy, education in schools and universities, finding information about claims, and finding sources for claims. The most controversial intervention is deleting misinformation. We discuss the potentials and risks of all interventions. Education-based interventions are perceived as the most helpful by misinformation experts. Interventions focused on providing evidence are also widely perceived as helpful. We discuss them as scalable and always available interventions that empower users to independently identify misinformation. We also introduce the Phase Model of Misinformation Interventions that helps practitioners make informed decisions about which interventions to focus on and how to best combine interventions.

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arXiv2024

Design Frictions on Social Media: Balancing Reduced Mindless Scrolling and User Satisfaction

Design features of social media platforms, such as infinite scroll, increase users' likelihood of experiencing normative dissociation -- a mental state of absorption that diminishes self-awareness and disrupts memory. This paper investigates how adding design frictions into the interface of a social media platform reduce mindless scrolling and user satisfaction. We conducted a study with 30 participants and compared their memory recognition of posts in two scenarios: one where participants had to react to each post to access further content and another using an infinite scroll design. Participants who used the design frictions interface exhibited significantly better content recall, although a majority of participants found the interface frustrating. We discuss design recommendations and scenarios where adding design frictions to social media platforms can be beneficial.

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arXiv2024

Reliability Criteria for News Websites

Misinformation poses a threat to democracy and to people's health. Reliability criteria for news websites can help people identify misinformation. But despite their importance, there has been no empirically substantiated list of criteria for distinguishing reliable from unreliable news websites. We identify reliability criteria, describe how they are applied in practice, and compare them to prior work. Based on our analysis, we distinguish between manipulable and less manipulable criteria and compare politically diverse laypeople as end users and journalists as expert users. We discuss 11 widely recognized criteria, including the following 6 criteria that are difficult to manipulate: content, political alignment, authors, professional standards, what sources are used, and a website's reputation. Finally, we describe how technology may be able to support people in applying these criteria in practice to assess the reliability of websites.

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arXiv2024

Writer-Defined AI Personas for On-Demand Feedback Generation

Compelling writing is tailored to its audience. This is challenging, as writers may struggle to empathize with readers, get feedback in time, or gain access to the target group. We propose a concept that generates on-demand feedback, based on writer-defined AI personas of any target audience. We explore this concept with a prototype (using GPT-3.5) in two user studies (N=5 and N=11): Writers appreciated the concept and strategically used personas for getting different perspectives. The feedback was seen as helpful and inspired revisions of text and personas, although it was often verbose and unspecific. We discuss the impact of on-demand feedback, the limited representativity of contemporary AI systems, and further ideas for defining AI personas. This work contributes to the vision of supporting writers with AI by expanding the socio-technical perspective in AI tool design: To empower creators, we also need to keep in mind their relationship to an audience.

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arXiv2022

A Comparative Evaluation of Interventions Against Misinformation: Augmenting the WHO Checklist

During the COVID-19 pandemic, the World Health Organization provided a checklist to help people distinguish between accurate and misinformation. In controlled experiments in the United States and Germany, we investigated the utility of this ordered checklist and designed an interactive version to lower the cost of acting on checklist items. Across interventions, we observe non-trivial differences in participants' performance in distinguishing accurate and misinformation between the two countries and discuss some possible reasons that may predict the future helpfulness of the checklist in different environments. The checklist item that provides source labels was most frequently followed and was considered most helpful. Based on our empirical findings, we recommend practitioners focus on providing source labels rather than interventions that support readers performing their own fact-checks, even though this recommendation may be influenced by the WHO's chosen order. We discuss the complexity of providing such source labels and provide design recommendations.

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arXiv2021

Beyond Participation: A Review of Co-Creation in Computing

New methods and technologies for engaging future users and other stakeholders in participatory (design) processes are being developed and proposed. Increasingly, researchers refer to co-creation in order to capture such approaches. However, how co-creation is being framed and understood across domains differs substantially. To better understand co-creation in computing, we conducted a literature review of all papers in the ACM Digital Library with co-creation or co-create in their abstracts. After an initial screening, we retained 62 for further analysis. We introduce a framework to analyze different notions of co-creation, distinguishing between co-creation target audiences, the roles of co-creators, the role of technology (as means or objective) and its results. We discuss the adoption of co-creation in domains such as learning, business, arts & culture, health, and the public sector. This paper contributes to the understanding of different approaches and conceptualizations of co-creation in computing and puts forward an agenda for future research.

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arXiv2021

The Explanatory Gap in Algorithmic News Curation

Considering the large amount of available content, social media platforms increasingly employ machine learning (ML) systems to curate news. This paper examines how well different explanations help expert users understand why certain news stories are recommended to them. The expert users were journalists, who are trained to judge the relevance of news. Surprisingly, none of the explanations are perceived as helpful. Our investigation provides a first indication of a gap between what is available to explain ML-based curation systems and what users need to understand such systems. We call this the Explanatory Gap in Machine Learning-based Curation Systems.

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arXiv2021

Auditing the Biases Enacted by YouTube for Political Topics in Germany

With YouTube's growing importance as a news platform, its recommendation system came under increased scrutiny. Recognizing YouTube's recommendation system as a broadcaster of media, we explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service. We present audits as an important tool to enforce such laws and to ensure that a system operates in the public's interest. To examine whether YouTube is enacting certain biases, we collected video recommendations about political topics by following chains of ten recommendations per video. Our findings suggest that YouTube's recommendation system is enacting important biases. We find that YouTube is recommending increasingly popular but topically unrelated videos. The sadness evoked by the recommended videos decreases while the happiness increases. We discuss the strong popularity bias we identified and analyze the link between the popularity of content and emotions. We also discuss how audits empower researchers and civic hackers to monitor complex machine learning (ML)-based systems like YouTube's recommendation system.

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