@article{baumlerHarnessingInterOrganizationalCollaboration2025,
title = {Harnessing {Inter}-{Organizational} {Collaboration} and {Automation} to {Combat} {Online} {Hate} {Speech}: {A} {Qualitative} {Study} with {German} {Reporting} {Centers}},
doi = {10.1145/3710991},
abstract = {In Germany and other countries, specialized non-profit reporting centers combat online hate speech by submitting criminal content to law enforcement agencies, forwarding deletion requests to social media platforms, and providing counseling to victims, thus contributing to the governance mechanism of content moderation as intermediaries between victims and various organizations. Whereas research in computer-supported cooperative work has extensively explored collaboration of and automation for content moderators, there are no works that focus on reporting centers. Based on expert interviews with their staff (N=15), this study finds that most German centers share a collaborative workflow, of which multiple tasks are heavily dependent on inter-organizational exchange. However, there are differences in their implementation of monitoring, content assessment, automation technology adoption, and external collaborators. As the centers are faced with diverse challenges, such as borderline case assessment, psychological burdens, limited visibility, conflicting goals with other actors, and manual repetitive work, our study contributes with nine implications for designing and researching supportive technologies. They provide suggestions for improving hate speech gathering and reporting, researching hate speech prioritization and assessment algorithms, and designing case processing systems. Beyond that, we outline directions for research on inter-organizational collaboration.},
journal = {Proceedings of the ACM: Human Computer Interaction (PACM): Computer-Supported Cooperative Work and Social Computing},
author = {Bäumler, Julian and Riebe, Thea and Kaufhold, Marc-André and Reuter, Christian},
year = {2025},
keywords = {AuswahlCrisis, Crisis, HCI, A-Paper, Ranking-CORE-A, Projekt-CYWARN, Projekt-CYLENCE},
}

@article{baumlerCyberHateAwareness2025,
title = {Cyber {Hate} {Awareness}: {Information} {Types} and {Technologies} {Relevant} to the {Law} {Enforcement} and {Reporting} {Center} {Domain}},
volume = {24},
issn = {2196-6826},
shorttitle = {Cyber hate awareness},
url = {https://www.degruyter.com/document/doi/10.1515/icom-2024-0062/html},
doi = {10.1515/icom-2024-0062},
abstract = {In Germany, both law enforcement agencies (LEAs) and dedicated reporting centers (RCs) engage in various activities to counter illegal online hate speech (HS). Due to the high volume of such content and against the background of limited resources, their personnel can be confronted with the issue of information overload. To mitigate this issue, information filtering, classification, prioritization, and visualization technologies offer great potential. However, a nuanced understanding of situational awareness is required to inform the domain-sensitive implementation of supportive technology and adequate decision-making. Although previous research has explored the concept of situational awareness in policing, it has not been studied in relation to online HS. Based on a qualitative research design employing a thematic analysis of qualitative expert interviews with practitioners from German LEAs and RCs (N=29), we will contribute to the state of research in human-computer interaction with a systematization of 23 information types of relevance for situational awareness of online HS in the law enforcement and RC domain. On that basis, we identify victim, perpetrator, context, evidence, legal, and threat awareness as domain-specific situational awareness sub-types and formulate ten implications for designing reporting, open-source intelligence, classification, and visual analytics tools.},
number = {1},
journal = {i-com – Journal of Interactive Media},
author = {Bäumler, Julian and Voronin, Georg and Kaufhold, Marc-André},
year = {2025},
keywords = {Crisis, HCI, Projekt-CYLENCE, Projekt-ATHENE-CyAware},
}

@inproceedings{baumlerYouthSensitiveHatefulContent2025,
address = {Yokohama, Japan},
series = {{CHI} '25},
title = {Towards {Youth}-{Sensitive} {Hateful} {Content} {Reporting}: {An} {Inclusive} {Focus} {Group} {Study} in {Germany}},
doi = {10.1145/3706598.3713542},
booktitle = {Proceedings of the {Conference} on {Human} {Factors} in {Computing} {Systems} ({CHI})},
publisher = {Association for Computing Machinery},
author = {Bäumler, Julian and Bader, Helen and Kaufhold, Marc-André and Reuter, Christian},
year = {2025},
keywords = {AuswahlCrisis, Crisis, HCI, Selected, Student, A-Paper, Ranking-CORE-A*, Projekt-CYLENCE, Projekt-ATHENE-CyAware},
annote = {Place: Yokohama, Japan},
}

@inproceedings{kaufholdCyberThreatAwareness2025,
series = {{CHI} '25},
title = {Cyber {Threat} {Awareness}, {Protective} {Measures} and {Communication} {Preferences} in {Germany}: {Implications} from {Three} {Representative} {Surveys} (2021-2024)},
doi = {10.1145/3706598.371379},
booktitle = {Proceedings of the {Conference} on {Human} {Factors} in {Computing} {Systems} ({CHI})},
publisher = {Association for Computing Machinery},
author = {Kaufhold, Marc-André and Bäumler, Julian and Bajorski, Marius and Reuter, Christian},
year = {2025},
keywords = {HCI, Selected, UsableSec, Security, A-Paper, Ranking-CORE-A*, AuswahlUsableSec, Projekt-ATHENE-CyAware},
}

@inproceedings{baumlerOnlineHateSpeech2024,
address = {Karlsruhe, Germany},
title = {Towards an {Online} {Hate} {Speech} {Classification} {Scheme} for {German} {Law} {Enforcement} and {Reporting} {Centers}: {Insights} from {Research} and {Practice}},
url = {https://dl.gi.de/items/2fa0ec97-d562-41d2-bab9-0b0539432c87},
doi = {10.18420/muc2024-mci-ws13-124},
abstract = {In Germany, both law enforcement agencies and dedicated reporting centers engage in various activities to counter illegal online hate speech. Due to the high volume of such content and against the background of limited resources, their personnel can be confronted with the issue of information overload. To mitigate this issue, technologies for information filtering, classification, prioritization, and visualization offer great potential. However, domainspecific classification schemes that differentiate subtypes of online hate speech are a prerequisite for the development of such assistive tools. There is a gap in research with regard to an empirically substantiated classification scheme for subtypes of hate speech for the German law enforcement and reporting center domain. Based on a review of relevant computer science publications (N=24) and qualitative interviews with practitioners (N=18), this work investigates practice-relevant subtypes of hate speech and finds that it is primarily differentiated with regard to targeted group affiliations, the conveyance of an immediate security threat, and criminal relevance. It contributes to the state of research with an empirically grounded online hate speech classification scheme for German law enforcement agencies and reporting centers (C1) and five implications for the user-centered design of hate speech classification tools (C2).},
language = {en},
booktitle = {Mensch und {Computer} 2024 - {Workshopband}},
publisher = {Gesellschaft für Informatik e.V.},
author = {Bäumler, Julian and Kaufhold, Marc-André and Voronin, Georg and Reuter, Christian},
year = {2024},
keywords = {HCI, UsableSec, Projekt-CYLENCE, Projekt-ATHENE-CyAware},
}

@article{baumlerSurveyMachineLearningModels,
title = {A survey of machine learning models and datasets for the multi-label classification of textual hate speech in english},
url = {https://arxiv.org/abs/2504.08609},
doi = {10.48550/arXiv.2504.08609},
abstract = {The dissemination of online hate speech can have serious negative consequences for individuals, online communities, and entire societies. This and the large volume of hateful online content prompted both practitioners', i.e., in content moderation or law enforcement, and researchers' interest in machine learning models to automatically classify instances of hate speech. Whereas most scientific works address hate speech classification as a binary task, practice often requires a differentiation into sub-types, e.g., according to target, severity, or legality, which may overlap for individual content. Hence, researchers created datasets and machine learning models that approach hate speech classification in textual data as a multi-label problem. This work presents the first systematic and comprehensive survey of scientific literature on this emerging research landscape in English (N=46). We contribute with a concise overview of 28 datasets suited for training multi-label classification models that reveals significant heterogeneity regarding label-set, size, meta-concept, annotation process, and inter-annotator agreement. Our analysis of 24 publications proposing suitable classification models further establishes inconsistency in evaluation and a preference for architectures based on Bidirectional Encoder Representation from Transformers (BERT) and Recurrent Neural Networks (RNNs). We identify imbalanced training data, reliance on crowdsourcing platforms, small and sparse datasets, and missing methodological alignment as critical open issues and formulate ten recommendations for research.},
journal = {arXiv},
author = {Bäumler, Julian and Blöcher, Louis and Frey, Lars-Joel and Chen, Xian and Bayer, Markus and Reuter, Christian},
year = {2024},
keywords = {Crisis, HCI, Projekt-ATHENE-CyAware, Projekt-CYLENCE, Student},
}

@inproceedings{kaufholdSecurityAdvisoryContent2024,
address = {Karlsruhe, Germany},
title = {Towards a {Security} {Advisory} {Content} {Retrieval} and {Extraction} {System} for {Computer} {Emergency} {Response} {Teams}},
url = {https://dl.gi.de/items/6ee00080-4245-44c0-ae9c-1a9cdea7fa3a},
doi = {10.18420/muc2024-mci-ws13-133},
abstract = {Computer Emergency Response Teams provide advisory, preventive, and reactive cybersecurity services for authorities, citizens, and businesses. However, their responsibility of establishing cyber situational awareness by monitoring and analyzing security advisories and vulnerabilities has become challenging due to the growing volume of information disseminated through public channels. Thus, this paper presents the preliminary design of a system for automatically retrieving and extracting security advisory documents from Common Security Advisory Framework (CSAF), HTML, and RSS sources. The evaluation with various security advisory sources (N=53) shows that the developed system can retrieve 90\% of the published advisory documents, which is a significant improvement over systems only relying on the retrieval from RSS feeds (30\%).},
language = {en},
booktitle = {Mensch und {Computer} 2024 - {Workshopband}},
publisher = {Gesellschaft für Informatik e.V.},
author = {Kaufhold, Marc-André and Bäumler, Julian and Koukal, Nicolai and Reuter, Christian},
year = {2024},
keywords = {HCI, UsableSec, Projekt-CYLENCE, Projekt-ATHENE-CyAware},
}

@inproceedings{kaufholdCYLENCEStrategiesTools2023,
address = {Rapperswil, Switzerland},
title = {{CYLENCE}: {Strategies} and {Tools} for {Cross}-{Media} {Reporting}, {Detection}, and {Treatment} of {Cyberbullying} and {Hatespeech} in {Law} {Enforcement} {Agencies}},
url = {https://dl.gi.de/items/0e0efe8f-64bf-400c-85f7-02b65f83189d},
doi = {10.18420/muc2023-mci-ws01-211},
abstract = {Despite the merits of public and social media in private and professional spaces, citizens and professionals are increasingly exposed to cyberabuse, such as cyberbullying and hate speech. Thus, Law Enforcement Agencies (LEA) are deployed in many countries and organisations to enhance the preventive and reactive capabilities against cyberabuse. However, their tasks are getting more complex by the increasing amount and varying quality of information disseminated into public channels. Adopting the perspectives of Crisis Informatics and safety-critical Human-Computer Interaction (HCI) and based on both a narrative literature review and group discussions, this paper first outlines the research agenda of the CYLENCE project, which seeks to design strategies and tools for cross-media reporting, detection, and treatment of cyberbullying and hatespeech in investigative and law enforcement agencies. Second, it identifies and elaborates seven research challenges with regard to the monitoring, analysis and communication of cyberabuse in LEAs, which serve as a starting point for in-depth research within the project.},
language = {de},
booktitle = {Mensch und {Computer} 2023 - {Workshopband}},
publisher = {Gesellschaft für Informatik e.V.},
author = {Kaufhold, Marc-André and Bayer, Markus and Bäumler, Julian and Reuter, Christian and Stieglitz, Stefan and Basyurt, Ali Sercan and Mirabaie, Milad and Fuchß, Christoph and Eyilmez, Kaan},
year = {2023},
keywords = {HCI, UsableSec, Projekt-CYLENCE},
}

@article{riebeValuesValueConflicts2023,
title = {Values and {Value} {Conflicts} in the {Context} of {OSINT} {Technologies} for {Cybersecurity} {Incident} {Response}: {A} {Value} {Sensitive} {Design} {Perspective}},
url = {https://link.springer.com/article/10.1007/s10606-022-09453-4},
doi = {10.1007/s10606-022-09453-4},
abstract = {The negotiation of stakeholder values as a collaborative process throughout technology development has been studied extensively within the fields of Computer Supported Cooperative Work and Human-Computer Interaction. Despite their increasing significance for cybersecurity incident response, there is a gap in research on values of importance to the design of open-source intelligence (OSINT) technologies for this purpose. In this paper, we investigate which values and value conflicts emerge due to the application and development of machine learning (ML) based OSINT technologies to assist cyber security incident response operators. For this purpose, we employ a triangulation of methods, consisting of a systematic survey of the technical literature on the development of OSINT artefacts for cybersecurity (N = 73) and an empirical value sensitive design case study, comprising semi-structured interviews with stakeholders (N = 9) as well as a focus group (N = 7) with developers. Based on our results, we identify implications relevant to the research on and design of OSINT artefacts for cybersecurity incident response.},
journal = {Computer Supported Cooperative Work: The Journal of Collaborative Computing (JCSCW)},
author = {Riebe, Thea and Bäumler, Julian and Kaufhold, Marc-André and Reuter, Christian},
year = {2023},
keywords = {HCI, Student, UsableSec, Security, A-Paper, Ranking-ImpactFactor, Ranking-CORE-B, Projekt-CROSSING, Projekt-ATHENE-SecUrban, Projekt-CYWARN},
}

@inproceedings{kaufholdImplementationProtectiveMeasures2022,
address = {Darmstadt},
series = {Mensch und {Computer} 2022 - {Workshopband}},
title = {The {Implementation} of {Protective} {Measures} and {Communication} of {Cybersecurity} {Alerts} in {Germany} - {A} {Representative} {Survey} of the {Population}},
url = {https://dl.gi.de/handle/20.500.12116/39061},
doi = {10.18420/muc2022-mci-ws01-228},
abstract = {Despite the merits of digitization in private and professional spaces, critical infrastructures and societies are increasingly exposed to cyberattacks. We conducted a representative survey with German citizens (N=1,093) to examine how they assess the current and future cyber threat situation as well as possible protective measures in cyberspace. Furthermore, we asked what information and channels citizens need to be aware of cyber threats. Our findings indicate that large proportions of the German population feel inadequately informed about cyber threats and tend to only apply enforced security measures by programs (e.g., updates) and services (e.g., two-factor authentication). Furthermore, institutions such as state-level Computer Emergency Response Teams (CERTs) are relatively unknown among the population and respondents showed little confidence in German security authorities to cope with largescale attacks and ultimately protect citizens. Still, our participants prefer to receive cybersecurity information via installed security applications, television channels, or emergency warning apps.},
language = {en},
booktitle = {Mensch und {Computer} 2022 - {Workshopband}},
publisher = {Gesellschaft für Informatik},
author = {Kaufhold, Marc-André and Bäumler, Julian and Reuter, Christian},
year = {2022},
keywords = {HCI, Student, UsableSec, Security, Projekt-ATHENE-SecUrban, Projekt-CYWARN},
}

@inproceedings{kuehnNotionRelevanceCybersecurity2022,
address = {Darmstadt},
series = {Mensch und {Computer} 2022 - {Workshopband}},
title = {The {Notion} of {Relevance} in {Cybersecurity}: {A} {Categorization} of {Security} {Tools} and {Deduction} of {Relevance} {Notions}},
url = {https://dl.gi.de/handle/20.500.12116/39072},
doi = {10.18420/muc2022-mci-ws01-220},
abstract = {Proper cybersecurity requires timely information to defend the IT infrastructure. In a dynamic field like cybersecurity, gathering up-to-date information is usually a manual, time-consuming, and exhaustive task. Automatic and usable approaches are supposed to be a solution to this problem, but for this, they require a notion of information relevance to distinguish relevant from irrelevant information. First, on the basis of a literature review, this paper proposes a novel cybersecurity tool categorization based on corresponding tool types with their respective definitions and core features. Second, it elaborates information used in each category and deduces notions of relevance. Third, it outlines how these findings informed the design of a security dashboard to guide computer emergency response team staff in identifying current threats in open source intelligence sources while mitigating information overload.},
language = {en},
booktitle = {Mensch und {Computer} 2022 - {Workshopband}},
publisher = {Gesellschaft für Informatik},
author = {Kuehn, Philipp and Bäumler, Julian and Kaufhold, Marc-André and Wendelborn, Marc and Reuter, Christian},
year = {2022},
keywords = {Student, UsableSec, Security, Projekt-ATHENE-SecUrban, Projekt-CYWARN},
}