From April 2 to 4, 2025, the Pre-CHI 2025 will take place in Siegen. Researchers from Germany and neighboring countries will have the opportunity to present and discuss their accepted CHI papers. Aditya Kumar Purohit, Artur Solomonik, Besjon Cifliku, Hendrik Heuer, and Jasmin Baake from the CAIS research program “Design of Trustworthy Artificial Intelligence” will also participate in the event and present their work.
About Pre-CHI
The ACM (Association for Computing Machinery) CHI conference on Human Factors in Computing Systems is the leading international conference in the field of Human-Computer Interaction and will take place from April 26 to May 1, 2025, in Yokohama, Japan. The Pre-CHI serves as a platform for presenting and discussing the accepted CHI 2025 papers. Particularly for the younger HCI community, the event offers a valuable opportunity to familiarize themselves with the requirements of an international top conference like CHI. The two-day event includes presentations, discussions, workshops, panel discussions, and demo sessions. Additionally, there will be ample time for informal exchanges during lunch or dinner and through laboratory tours. This year’s Pre-CHI is organized by the University of Siegen in collaboration with the Institute for Social Informatics Bonn (IISI) and the non-profit Society for Digital and Sustainable Collaboration (DNZ).
Contributions by our researchers
The following papers, involving CAIS researchers, will be presented at the Pre-CHI:
“Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations”.
Authors: David Hartmann (Weizenbaum Institute Berlin, Technical University Berlin), Amin Oueslati (Hertie School Berlin), Dimitri Staufer (Technical University Berlin), Lena Pohlmann (Weizenbaum Institute Berlin, Technical University Berlin), Simon Munzert (Hertie School Berlin), Hendrik Heuer (Center for Advanced Internet Studies CAIS, University of Wuppertal)
Abstract: 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.
Link to the paper: https://arxiv.org/abs/2503.01623
“Social Media for Activists: Reimagining Safety, Content Presentation, and Workflows”
Authors: Anna Ricarda Luther (Institute for Information Management Bremen GmbH, University of Bremen), Hendrik Heuer (Center for Advanced Internet Studies (CAIS), University of Wuppertal), Stephanie Geise (Centre for Media, Communication and Information Research (ZeMKI), University of Bremen), Sebastian Haunss (Research Center on Inequality and Social Policy (SOCIUM), University of Bremen), Andreas Breiter (Institute for Information Management Bremen GmbH, University of Bremen)
Abstract: 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.
Link to the paper: https://arxiv.org/abs/2503.12924
“Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social Media”
Autoren: Luca-Maxim Meinhardt (University of Ulm), Maryam Elhaidary (University of Ulm), Mark Colley (University of Ulm, UCL Interaction Centre, London), Michael Rietzler (University of Ulm), Jan Ole Rixen (University of Ulm, Karlsruhe Institute of Technology), Aditya Kumar Purohit (Center for Advanced Internet Studies (CAIS)), Enrico Rukzio (University of Ulm)
Abstract: Infinite scrolling on social media platforms is designed to encourage prolonged engagement, leading users to spend more time than desired, which can provoke negative emotions. Interventions to mitigate infinite scrolling have shown initial success, yet users become desensitized due to the lack of contextual relevance. Understanding how contextual factors influence intervention effectiveness remains underexplored. We conducted a 7-day user study (N=72) investigating how these contextual factors affect users’ reactance and responsiveness to interventions during infinite scrolling. Our study revealed an interplay, with contextual factors such as being at home, sleepiness, and valence playing significant roles in the intervention’s effectiveness. Low valence coupled with being at home slows down the responsiveness to interventions, and sleepiness lowers reactance towards interventions, increasing user acceptance of the intervention. Overall, our work contributes to a deeper understanding of user responses toward interventions and paves the way for developing more effective interventions during infinite scrolling.
Link zum Paper: https://arxiv.org/pdf/2501.11814
“Social Media Journeys – Mapping Platform Migration” (Late Breaking Paper)
Authors: Artur Solomonik (Center for Advanced Internet Studies (CAIS)), Hendrik Heuer (Center for Advanced Internet Studies (CAIS), University of Wuppertal)
Abstract: 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.
Link to the paper: https://arxiv.org/abs/2503.12924
“This could save us months of work” – Use Cases of AI and Automation Support in Investigative Journalism” (Late Breaking Paper)
Authors: Besjon Cifliku (Center for Advanced Internet Studies (CAIS)), Hendrik Heuer (Center for Advanced Internet Studies (CAIS), Universität Wuppertal)
Abstract: 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. 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 for automation and presented a prototype 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. We provide guidelines on how investigative journalism can benefit from AI and automation.
Link to the paper: https://arxiv.org/abs/2503.16011