Misinformation interventions are often evaluated under ideal conditions, yet real-world systems are rarely flawless. We report on an online experiment (N = 1, 004) comparing five state-of-the-art interventions – inoculation, accuracy prompt, community note, fact-check, and indicators – across TikTok, Telegram, and X. We examined efficacy and user perceptions under flawless and erroneous implementations. Misinformation accompanied by fact-checks and indicators was rated as significantly less accurate, while community notes showed weaker effects. Modality did not significantly influence intervention efficacy and had only minor effects on user acceptance. Community notes, fact-checks, and indicators were rated as more helpful but also more annoying than the less informative accuracy prompts. Notably, the efficacy of interventions disappeared under erroneous conditions. This highlights the crucial role of intervention quality in fostering trust and acceptance. Our findings provide (1) a cross-platform evaluation of interventions and (2) empirical evidence that accuracy and reliability are crucial in complex social media environments.

Abstract Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning. However, many active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. This limitation reduces their utility in the increasingly relevant few-shot scenarios, where the instance selection has a substantial impact. To address this, we introduce ActiveLLM, a novel active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. We demonstrate that ActiveLLM significantly enhances the classification performance of BERT classifiers in few-shot scenarios, outperforming traditional active learning methods as well as improving the few-shot learning methods ADAPET, PERFECT, and SetFit. Additionally, ActiveLLM can be extended to non-few-shot scenarios, allowing for iterative selections. In this way, ActiveLLM can even help other active learning strategies to overcome their cold-start problem. Our results suggest that ActiveLLM offers a promising solution for improving model performance across various learning setups.

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’, e.g., 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, revealing 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 12 recommendations for research. Whereas transparency, data source diversity, conceptual rigor, label-set consolidation, inter-annotator agreement, and model validation on existing datasets warrant more attention, multi-label tasks related to criminal relevance, enhancing robustness through ensembling, and exploring performance gains through large foundation models represent promising avenues for future work.

Youth are particularly likely to encounter hateful internet content, which can severely impact their well-being.While most social media provide reporting mechanisms, in several countries, severe hateful content can alternatively be reported to law enforcement or dedicated reporting centers.However, in Germany, many youth never resort to reporting.While research in human-computer interaction has investigated adults' views on platform-based reporting, youth perspectives and platform-independent alternatives have received little attention.By involving a diverse group of 47 German adolescents and young adults in eight focus group interviews, we investigate how youth-sensitive reporting systems for hateful content can be designed.We explore German youth's reporting barriers, fnding that on platforms, they feel particularly discouraged by defcient rule enforcement and feedback, while platform-independent alternatives are rather unknown and perceived as time-consuming and disruptive.We further elicit their requirements for platformindependent reporting tools and contribute with heuristics for designing youth-sensitive and inclusive reporting systems.

Internet of Things (IoT) devices have become increasingly important within the smart home domain, making the security of the devices a critical aspect. The majority of IoT devices are black-box systems running closed and pre-installed firmware. This raises concerns about the trustworthiness of these devices, especially considering that some of them are shipped with a microphone or a camera. Remote attestation aims at validating the trustworthiness of these devices by verifying the integrity of the software. However, users cannot validate whether the attestation has actually taken place and has not been manipulated by an attacker, raising the need for HCI research on trust and understandability. We conducted a qualitative study with 35 participants, investigating trust in the attestation process and whether this trust can be improved by additional explanations in the application. We developed an application that allows users to attest a smart speaker using their smartphone over an audio channel to identify the attested device and observe the attestation process. In order to observe the differences between the applications with and without explanations, we performed A/B testing. We discovered that trust increases when additional explanations of the technical process are provided, improving the understanding of the attestation process.

The rise of fake news and misinformation in the digital age poses serious risks for individuals and society, particularly during crises like the Israel-Hamas war, the Russian-Ukraine war, or the COVID-19 pandemic. Misinformation serves as a tool to manipulate public opinion and create discord. Vulnerability to manipulation increases in online spaces during crises, where authoritative information is scarce. Emergency management, public health, political and public administration, as well as media professionals and citizens express concern and seek solutions to enhance information quality during such critical times. This article highlights user-centred approaches to countering fake news and misinformation, tracing their historical evolution from ancient Greece to the present, focussing on their relevance during the COVID-19 crisis and in contemporary information warfare. It describes the vulnerability of audiences to misinformation and outlines prevailing trends in user-centred countermeasures. This article also introduces recent research on the effectiveness of media literacy interventions on truth discernment, a cross-cultural comparison of the perception of negative consequences and the injunctive norm, a comparison of video and text to promote lateral reading in adolescents, content-specific indicators on Twitter from a user perspective, a learning system for detecting misinformation as well as ethical and security considerations in automated detection.

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.

The ongoing challenge of misinformation on social media motivates ongoing efforts to find effective countermeasures. In this study, we evaluated the potential of personalised nudging to reduce the sharing of misinformation on social media, as personalised support has been successfully applied in other areas of critical information handling. In an online experiment (N = 396) exposing users to social media posts, we assessed the degree of misinformation sharing between groups receiving (1) no nudges, (2) non-personalised nudges, and (3) personalised nudges. Personalisation was based on three psychometric dimensions - general decision-making style, consideration of future consequences, need for cognition - to assign the most appropriate nudge from a pool of five nudges. The results showed significant differences (p < .05) between all three groups, with the personalised nudge group sharing the least misinformation. Detailed analyses at the nudge level revealed that one nudge was universally effective and two nudges were effective only in their personalised form. The results generally confirm the potential of personalisation, although the effect is limited in scope. These findings shed light on the nuanced results of nudging studies, highlight the benefits of personalisation, and raise ethical considerations regarding the privacy implications of personalisation and those inherent in nudges.

In conflict-ridden environments, timely and accurate information is critical for those dealing with the dynamic of events. When individuals have to flee, it becomes evident that refugees frequently rely on information and communication technologies (ICT) for information acquisition, travel coordination, and maintaining connections with related parties. Based on 17 interviews, this research explores how Ukrainian refugees, who sought protection in Germany due to the 2022 Russian full-scale invasion, use ICT before, during, and after their flight. By providing empirical findings, the results show in depth how contextual factors, such as infrastructural instability, privacy concerns, and an advanced digitalization, interrelate with user behaviors. Analyzing the multifaceted civilian ICT use in the context of war and flight, this exploratory research contributes to the existing research on HCI in migration contexts and connects to several topics of CSCW. By contrasting case specifics, this work highlights what makes Ukraine a special case in this research area. Furthermore, this paper examines both existing and emerging affordances of ICT in the context of flight and identifies the crucial role of messenger groups for information gathering in all phases of the flight. Lastly, collaborative dimensions of the identified affordances are discussed.

Activists’ use of ICTs in contexts affected by violence poses challenges like digital surveillance. Based on 92 interviews with activists from Bolivia, Cameroon, Costa Rica, Colombia, India, Myanmar, and Nicaragua, this study explores their protection and coping strategies against technology-facilitated violence. Findings show limited emphasis on digital security and privacy-enhancing technologies, primarily due to the usability and familiarity with popular applications, coupled with relative unfamiliarity with other features. Generally, some precautions, like avoiding real-time location sharing, are common. While many protection strategies are similar, some are shaped by local factors like internet shutdowns and different adversaries’ capabilities. The findings demonstrate that some tools and features, such as VPN, may offer protection but also pose risks, e.g., in the case of criminalization, highlighting the crucial role of local context. Designing secure and usable features that account for the varying levels of limitations, such as poor connectivity and risks activists face, is essential.

Misinformation poses a recurrent challenge for video-sharing platforms (VSPs) like TikTok. Obtaining user perspectives on digital interventions addressing the need for transparency (e.g., through indicators) is essential. This article offers a thorough examination of the comprehensibility, usefulness, and limitations of an indicator-based intervention from an adolescents’ perspective. This study (N = 39; aged 13-16 years) comprised two qualitative steps: (1) focus group discussions and (2) think-aloud sessions, where participants engaged with a smartphone-app for TikTok. The results offer new insights into how video-based indicators can assist adolescents’ assessments. The intervention received positive feedback, especially for its transparency, and could be applicable to new content. This paper sheds light on how adolescents are expected to be experts while also being prone to video-based misinformation, with limited understanding of an intervention’s limitations. By adopting teenagers’ perspectives, we contribute to HCI research and provide new insights into the chances and limitations of interventions for VSPs.

Computer Emergency Response Teams (CERTs) provide advisory, preventive and reactive cybersecurity services for authorities, citizens, and businesses. However, their responsibility of monitoring, analyzing, and communicating cyber threats have become challenging due to the growing volume and varying quality of information disseminated through public channels. Based on a design case study conducted from 2021 to 2023, this paper combines three iterations of expert interviews, design workshops and cognitive walkthroughs to design an automated, cross-platform and real-time cybersecurity dashboard. By adopting the notion of cyber situational awareness, the study extracts user requirements and design heuristics for enhanced threat awareness and mission awareness in CERTs, discussing the aspects of source integration, data management, customizable visualization, relationship awareness, information assessment, software integration, (inter-)organizational collaboration, and communication of stakeholder warnings.

Adversarial examples, capable of misleading machine learning models into making erroneous predictions, pose significant risks in safety-critical domains such as crisis informatics, medicine, and autonomous driving. To counter this, we introduce a novel textual adversarial example method that identifies falsely learned word indicators by leveraging explainable AI methods as importance functions on incorrectly predicted instances, thus revealing and understanding the weaknesses of a model. To evaluate the effectiveness of our approach, we conduct a human and a transfer evaluation and propose a novel adversarial training evaluation setting for better robustness assessment. While outperforming current adversarial example and training methods, the results also show our method’s potential in facilitating the development of more resilient transformer models by detecting and rectifying biases and patterns in training data, showing baseline improvements of up to 23 percentage points in accuracy on adversarial tasks. The code of our approach is freely available for further exploration and use.

Farm Management Information Systems (FMIS) are an important core component of modern farming companies as they allow, e.g. to document activities, create fertilisation plans, and feed digital equipment with required data. Since the entire agricultural sector is an essential component of food production, high standards of resilience should be established in the involved companies. Accordingly, the used software should also be designed with high standards on reliability and crisis capability. Based on a literature review, we found that software for farmers with certain resilience needs is lacking. Thus, we designed and evaluated a new FMIS concept with the user-centred design method. By conducting focus groups (two rounds, total N = 57) in 2017 and 2019, we raised specific front-end and back-end requirements of farmers. Based on the requirements, we developed our concept for both front- and back-end in terms of a decentralised and offline-working FMIS. Through the evaluation with practitioners (N = 16) of the implemented concept, we derived findings and implications, highlighting the need for privacy, stability, and offline-capability, as well as the UI-requirement to be supportive, e.g. with easy to understand icons and terms.

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.

Browser cookies, especially those from third parties, pose a threat to individual privacy. While it is possible in principle to control the number of cookies accepted, this choice is often neither usable nor truly informed. To address this issue, this study used semi-structured interviews (N=19) to identify attitudes and user requirements to develop an alternative personalised cookie banner, which was evaluated in an online experiment (N=157). The cookie banner explanations were tailored to the privacy knowledge of three groups of users: low, medium and high. The online experiment measured cookie choices and perceived usability of the cookie banner across three groups: an experimental group that viewed the novel cookie banner with personalisation (personalised privacy assistant), a control group that viewed the novel cookie banner without personalisation (privacy assistant) and a control group that viewed the standard cookie banner provided by the website. The results indicate that the novel cookie banner (with or without personalisation) generally resulted in significantly fewer accepted cookies and increased usability compared to the standard cookie window. In addition, the personalised cookie banner resulted in significantly fewer accepted cookies and higher usability than the non-personalised cookie banner. These results suggest that tailoring cookie banners to users' privacy knowledge can be an effective approach to empowering users to make informed choices and better protect their privacy.

In many applications and websites people use in their everyday life, their privacy and data are threatened, e.g., by script tracking during browsing. Although researchers and companies have developed privacy-enhancing technologies (PETs), they are often difficult to use for lay users. In this paper, we conducted a literature review to classify users into different extit{support personas} based on their privacy competence and privacy concern. With developers of PETs in mind, support personas were envisioned to facilitate the customization of software according to the support needs of different users. In order to demonstrate the usefulness of support personas and based on workshop sessions with 15 participants, we designed a browser extension that supports users with the issue of script tracking by providing different user interfaces for different support personas. The following qualitative evaluation with 31 participants showed that the developed UI elements worked as intended for the different support personas. Therefore, we conclude the concept of support personas is useful in the development process of usable applications that enhance the privacy of the users while also educating them and thus potentially increasing their privacy literacy.

In many scenarios, users have to communicate sensitive data with third parties such as doctors, lawyers, insurance companies, social workers, or online shops. Handing over personal data is necessary to use those services, but delegating tasks to increase efficiency still poses the risk that personal data might be leaked. To minimize this risk and further enhance the privacy of users, we propose an interaction concept that uses layered encryption of messages to provide a trade-off between privacy and usability. Users can choose which data is additionally encrypted in an inner layer, e.g. only for the eyes of their doctor, and which data is available in an outer (encrypted or unencrypted) layer for all staff members. Another benefit is the hiding of sensitive data from package inspection or crawling algorithms via emails, while less critical parts can still be processed by these systems via the partial access. To investigate this concept, we derive relevant use cases for form-based communication via email from a quantitative pre-study with 1011 participants, showing that general practitioners are the most suitable use case. We developed demonstrators for this use case and evaluated them in a qualitative study with 42 participants. Our results show that the possibility of minimizing the propagation of sensitive data through additional encryption is highly appreciated and the usage of form-based communication is a promising approach for digital transformation.

The field of cysec is evolving fast. Security professionals are in need of intelligence on past, current and —ideally — upcoming threats, because attacks are becoming more advanced and are increasingly targeting larger and more complex systems. Since the processing and analysis of such large amounts of information cannot be addressed manually, cysec experts rely on machine learning techniques. In the textual domain, pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) have proven to be helpful as they provide a good baseline for further fine-tuning. However, due to the domain-knowledge and the many technical terms in cysec, general language models might miss the gist of textual information. For this reason, we create a high-quality dataset 1 and present a language model 2 specifically tailored to the cysec domain that can serve as a basic building block for cybersecurity systems. The model is compared on 15 tasks: Domain-dependent extrinsic tasks for measuring the performance on specific problems, intrinsic tasks for measuring the performance of the internal representations of the model, as well as general tasks from the SuperGLUE benchmark. The results of the intrinsic tasks show that our model improves the internal representation space of domain words compared with the other models. The extrinsic, domain-dependent tasks, consisting of sequence tagging and classification, show that the model performs best in cybersecurity scenarios. In addition, we pay special attention to the choice of hyperparameters against catastrophic forgetting, as pre-trained models tend to forget the original knowledge during further training.

About half a billion households are expected to use smart home systems by 2025. Although many IoT sensors, such as smoke detectors or security cameras, are available and governmental crisis warning systems are in place, little is known about how to warn appropriately in smart home environments. We created a Raspberry Pi based prototype with a speaker, a display, and a connected smart light bulb. Together with a focus group, we developed a taxonomy for warning messages in smart home environments, dividing them into five classes with different stimuli. We evaluated the taxonomy using the Experience Sampling Method (ESM) in a field study at participants’ (N = 13) homes testing 331 warnings. The results show that taxonomy-based warning stimuli are perceived to be appropriate and participants could imagine using such a warning system. We propose a deeper integration of warning capabilities into smart home environments to enhance the safety of citizens.

From the perspective of end-users, IoT devices behave like a black box: As long as they work as intended, users will not detect any compromise. Users have minimal control over the software. Hence, it is very likely that the user misses that illegal recordings and transmissions occur if a security camera or a smart speaker is hacked. In this paper, we present SCAtt-man, the first remote attestation scheme that is specifically designed with the user in mind. SCAtt-man deploys software-based attestation to check the integrity of remote devices, allowing users to verify the integrity of IoT devices with their smartphones. The key novelty of SCAtt-man resides in the utilization of user-observable side-channels such as light or sound in the attestation protocol. Our proof-of-concept implementation targets a smart speaker and an attestation protocol that is based on a data-over-sound protocol. Our evaluation demonstrates the effectiveness of \toolname against a variety of attacks and its usability based on a user study with 20 participants.

The use of Open Source Intelligence (OSINT) to monitor and detect cybersecurity threats is gaining popularity among Cybersecurity Emergency or Incident Response Teams (CERTs/CSIRTs). They increasingly use semi-automated OSINT approaches when monitoring cyber threats for public infrastructure services and incident response. Most of the systems use publicly available data, often focusing on social media due to timely data for situational assessment. As indirect and affected stakeholders, the acceptance of OSINT systems by users, as well as the conditions which influence the acceptance, are relevant for the development of OSINT systems for cybersecurity. Therefore, as part of the ethical and social technology assessment, we conducted a survey (N=1,093), in which we asked participants about their acceptance of OSINT systems, their perceived need for open source surveillance, as well as their privacy behavior and concerns. Further, we tested if the awareness of OSINT is an interactive factor that affects other factors. Our results indicate that cyber threat perception and the perceived need for OSINT are positively related to acceptance, while privacy concerns are negatively related. The awareness of OSINT, however, has only shown effects on people with higher privacy concerns. Here, particularly high OSINT awareness and limited privacy concerns were associated with higher OSINT acceptance. Lastly, we provide implications for further research and the use of OSINT systems for cybersecurity by authorities. As OSINT is a framework rather than a single technology, approaches can be selected and combined to adhere to data minimization and anonymization as well as to leverage improvements in privacy-preserving computation and machine learning innovations. Regarding the use of OSINT, the results suggest to favor approaches that provide transparency to users regarding the use of the systems and the data they gather.

Nudging users to keep them secure online has become a growing research field in cybersecurity. While existing approaches are mainly blackbox based, showing aggregated visualisations as one-size-fits-all nudges, personalisation turned out promising to enhance the efficacy of nudges within the high variance of users and contexts. This article presents a disaggregated whitebox-based visualisation of critical information as a novel nudge. By segmenting users according to their decision-making and information processing styles, we investigate if the novel nudge is more effective for specific users than a common black-box nudge. Based on existing literature about critical factors in password security, we designed a dynamic radar chart and parallel coordinates as disaggregated visualisations. We evaluated the short-term effectiveness and users' perception of the nudges in a think-aloud prestudy and a representative online evaluation (N=1.012). Our findings suggest that dynamic radar charts present a moderately effective nudge towards stronger passwords regarding short-term efficacy and are appreciated particularly by players of role-playing games.

Business collaboration in the era of digital transformation requires the exchange of operational data. Since data are hardly controllable once they have been published or shared with others, it is highly important that users are clearly informed about who has access to which data and how certain settings can prevent the disclosure of sensitive data. However, giving end users more control over their data through increased transparency could also lead to information overload. This is particularly true in the field of agriculture, where tight schedules put pressure on employees of small enterprises. We conduct an empirical prestudy with 52 German farmers to investigate current data sharing scenarios. From these insights, we derive requirements and a concept for data sharing solutions providing data flow transparency for users. To investigate the behavior of users and the effects of transparent UI controls, we evaluate a prototype with 18 persons. Our evaluation shows that farmers demand flexible and secure tools that adjust to their workflows. Also, data should be stored and processed locally, granting farmers data sovereignty. Although the controls require additional effort, the evaluated transparent controls for data disclosure are easy to use and raise user awareness.

In the last decades, research has shown that both technical solutions and user perceptions are important to improve security and privacy in the digital realm. The field of ‘usable security’ already started to emerge in the mid-90s, primarily focussed on password and email security. Later on, the research field of ”usable security and privacy” evolved and broadened the aim to design concepts and tools to assist users in enhancing their behaviour with regard to both privacy and security. Nevertheless, many user interventions are not as effective as desired. Because of highly diverse usage contexts, leading to different privacy and security requirements and not always to one-size-fits-all approaches, tailorability is necessary to address this issue. Furthermore, transparency is a crucial requirement, as providing comprehensible information may counter reactance towards security interventions. This article first provides a brief history of the research field in its first quarter-century and then highlights research on the transparency and tailorability of user interventions. Based on this, this article then presents six contributions with regard to (1) privacy concerns in times of COVID-19, (2) authentication on mobile devices, (3) GDPR-compliant data management, (4) privacy notices on websites, (5) data disclosure scenarios in agriculture, as well as (6) rights under data protection law and the concrete process should data subjects want to claim those rights. This article concludes with several research directions on user-centred transparency and tailorability.

Abstract Within CSCW and HCI, an increasing body of literature has been demonstrating the essential relevance of infrastructures and infrastructuring to the work of people engaging in technologically mediated nomadicity. Tech Nomads – or T-Nomads, as they are sometimes called – not only rely ontechnological, human, andenvironmentalinfrastructural components – such as Wi-Fi, technical support, space, and basic resources such as light and power outlets – but they also have to engage in infrastructuring to mobilise their workplaces and effectively accomplish work in and across different locations. In this article, we bring an infrastructuring perspective to understanding nomadic practices concerning the organisation of complex collaborative events. We introduce findings from a long-term investigation focusing on how infrastructures are re-instantiated with the help of digital technologies, according to emerging demands from T-Nomads. Our findings demonstrate the need for a ‘non-essentialist’ approach to nomadicity, one which recognises the character of nomadic work and its varied aspects in different contexts. We extend the infrastructuring literature by demonstrating how infrastructuring work is done in a complex collaborative initiative, as the organisation of the annual European Social Forum.

Concise instruments to determine privacy personas – typical privacy-related user groups – are not available at present. Consequently, we aimed to identify them on a privacy knowledge–privacy behavior ratio based on a self-developed instrument. To achieve this, we conducted an item analysis (N = 820) and a confirmatory factor analysis (CFA) (N = 656) of data based on an online study with German participants. Starting with 81 items, we reduced those to an eleven-item questionnaire with the two scales privacy knowledge and privacy behavior. A subsequent cluster analysis (N = 656) revealed three distinct user groups: (1) Fundamentalists scoring high in privacy knowledge and behavior, (2) Pragmatists scoring average in privacy knowledge and behavior and (3) Unconcerned scoring low in privacy knowledge and behavior. In a closer inspection of the questionnaire, the CFAs supported the model with a close global fit based on RMSEA in a training and to a lesser extent in a cross-validation sample. Deficient local fit as well as validity and reliability coefficients well below generally accepted thresholds, however, revealed that the questionnaire in its current form cannot be considered a suitable measurement instrument for determining privacy personas. The results are discussed in terms of related persona conceptualizations, the importance of a methodologically sound investigation of corresponding privacy dimensions and our lessons learned.

Vulnerability databases are one of the main information sources for IT security experts. Hence, the quality of their information is of utmost importance for anyone working in this area. Previous work has shown that machine readable information is either missing, incorrect, or inconsistent with other data sources. In this paper, we introduce a system called Overt Vulnerability source ANAlysis (OVANA), which analyzes the information quality of vulnerability databases utilizing state-of-the-art machine learning (ML) and natural language processing (NLP) techniques, searches the free-form description for relevant information missing from structured fields, and updates it accordingly. Our paper exemplifies that on the National Vulnerability Database, showing that OVANA is able to improve the information quality by 51.23% based on the indicators of accuracy, completeness, and uniqueness. Moreover, we present information which should be incorporated into the structured fields to increase the uniqueness of vulnerability entries and improve the discriminability of different vulnerability entries. The identified information from OVANA enables a more targeted vulnerability search and provides guidance for IT security experts in finding relevant information in vulnerability descriptions for severity assessment.

Reliable IT-based communication in agriculture is becoming increasingly important for regular operations. For example, if a farmer is in the field during a network outage, such as a failure of the mobile network, an alternative communication channel is needed to continue to connect to IT components and required data. With increasing digitalization, Low Power Wide Area Network (LPWAN) technologies are being used more and more frequently, e.g. for sensor networks. The LPWAN technologies offer a high range and can be used autonomously for the most part, but do not allow classic TCP/IP communication. In this work, a popular LPWAN technology, namely LoRaWAN, is experimentally supplemented by AX.25 on OSI layer 2 (Data Link Layer) to allow end devices TCP/IP-based communication over long distances. The evaluation shows that classic low-bandwidth applications are thus functional and can enable reliable, crisis-capable data transmission.

During crises such as the COVID-19 pandemic, people spontaneously initiate support groups, while established organizations like soccer clubs set non-regular goals, both offering help. Interested in the coordination of such help and potential challenges of collaboration, we conducted a virtual ethnography of a multi-level network located in Germany. We focused on aims, activities, and technological mediation, with Activity Theory as theoretical framework. Our findings show that the organizational aim of coordinating help was successfully achieved by connecting heterogeneous actors through digitization and institutionalization. Enabled by the context of the COVID-19 pandemic crisis, the network acted virtually, but was also able to integrate analog spaces of help. We identified six crucial implications regarding the use of technology and collaboration for building a successful volunteering network.

Besides the merits of increasing digitization and interconnectedness in private and professional spaces, critical infrastructures and societies are more and more exposed to cyberattacks. In order to enhance the preventative and reactive capabilities against cyberattacks, Computer Emergency Response Teams (CERTs) are deployed in many countries and organizations. In Germany, CERTs in the public sector operate on federal and state level to provide information security services for authorities, citizens, and enterprises. Their tasks of monitoring, analyzing, and communicating threats and incidents is getting more complex due to the increasing amount of information disseminated into public channels. By adopting the perspectives of Computer-Supported Cooperative Work (CSCW) and Crisis Informatics, we contribute to the study of organizational structures, technology use, and the impact on collaborative practices in and between state CERTs with empirical research based on expert interviews with representatives of German state CERTs (N=15) and supplementary document analyses (N=25). We derive design and policy implications from our findings, including the need for interoperable and modular architecture, a shift towards service level agreements, cross-platform monitoring and analysis of incident data, use of deduplication techniques and standardized threat exchange formats, a reduction of resource costs through process automation, and transparent reporting and tool structures for information exchange.

Since 2015, an increased number of asylum seekers is coming to Europe. These migration movements increasingly rely on digital infrastructure, such as mobile internet access and online services, in order to reach their targeted destination countries. Asylum seekers often use smartphones for information and communication purposes. Even though there are many positive aspects in the use of such technologies, researchers have to consider the perceived risks of this specific user group. This work aims at investigating the use of mobile information technologies by asylum seekers during their flight, especially taking privacy into account. Thus, it examines asylum seekers' digital privacy perceptions and identifies privacy protection behaviors by conducting a qualitative interview study with 14 asylum seekers who applied for asylum in Germany. The results show that asylum seekers are often aware of the various risks deriving from the use of smartphones and ICT, such as surveillance and persecution by state or non-state actors as well as extortion by criminals. Based on this, this work furthermore outlines different strategies used to manage these risks. Since the lack of privacy and trust leads to avoidance behavior, the insights of this study provide valuable information for the design of assistance apps and collaboration platforms, which appropriately address the specific needs for digital privacy in the context of flight, or for the conception of privacy-enhancing technologies helping to achieve this.

Abstract Technological progress can disrupt domains and change the way we work and collaborate. This paper presents a qualitative study with 52 German farmers that investigates the impact of the ongoing digitalization process in agriculture and discusses the implications for privacy research. As in other domains, the introduction of digital tools and services leads to the data itself becoming a resource. Sharing this data with products along the supply chain is favored by retailers and consumers, who benefit from traceability through transparency. However, transparency can pose a privacy risk. Having insight into the business data of others along the supply chain provides an advantage in terms of market position. This is particularly true in agriculture, where there is already a significant imbalance of power between actors. A multitude of small and medium-sized farming businesses are opposed by large upstream and downstream players that drive technological innovation. Further weakening the market position of farmers could lead to severe consequences for the entire sector. We found that on the one hand, privacy behaviors are affected by adoption of digitalization, and on the other hand, privacy itself influences adoption of digital tools. Our study sheds light on the emerging challenges for farmers and the role of privacy in the process of digitalization in agriculture.

The growing importance of social media in conflicts and crises is accompanied by an ever-increasing research interest in the crisis informatics field in order to identify potential benefits and develop measures against the technology’s abuse. This special issue sets out to give an overview of current research on the use of social media in conflicts and crises. In doing so, it focuses on both good and malicious aspects of social media and includes a variety of papers of conceptual, theoretical and empirical nature. In six sections, the special issue presents an overview of the field, analytical methods, technical challenges, current advancements and the accepted papers before concluding. Specific topics range from cyber deception over information trustworthiness to mining and near-real-time processing of social media data.

The research field of crisis informatics examines, amongst others, the potentials and barriers of social media use during conflicts and crises. Social media allow emergency services to reach the public easily in the context of crisis communication and receive valuable information (e.g. pictures) from social media data. However, the vast amount of data generated during large-scale incidents can lead to issues of information overload and quality. To mitigate these issues, this paper proposes the semi-automatic creation of alerts including keyword, relevance and information quality filters based on cross-platform social media data. We conducted empirical studies and workshops with emergency services across Europe to raise requirements, then iteratively designed and implemented an approach to support emergency services, and performed multiple evaluations, including live demonstrations and field trials, to research the potentials of social media-based alerts. Finally, we present the findings and implications based on semi-structured interviews with emergency services, highlighting the need for usable configurability and white-box algorithm representation.

Since the emergence of so-called fake news on the internet and in social media, platforms such as Facebook have started to take countermeasures, and researchers have begun looking into this phenomenon from a variety of perspectives. A large number of scientific work has investigated ways to detect fake news automatically. Less attention has been paid to the subsequent step, i.e., what to do when you are aware of the inaccuracy of claims in social media. This work takes a user-centered approach on means to counter identified mis- and disinformation in social media. We conduct a three-step study design on how approaches in social media should be presented to respect the users' needs and experiences and how effective they are. As our first step, in an online survey representative for some factors to the German adult population, we enquire regarding their strategies on handling information in social media, and their opinion regarding possible solutions --- focusing on the approach of displaying a warning on inaccurate posts. In a second step, we present five potential approaches for countermeasures identified in related work to interviewees for qualitative input. We discuss (1) warning, (2) related articles, (3) reducing the size, (4) covering, and (5) requiring confirmation. Based on the interview feedback, as the third step of this study, we select, improve, and examine four promising approaches on how to counter misinformation. We conduct an online experiment to test their effectiveness on the perceived accuracy of false headlines and also ask for the users' preferences. In this study, we find that users welcome warning-based approaches to counter fake news and are somewhat critical with less transparent methods. Moreover, users want social media platforms to explain why a post was marked as disputed. The results regarding effectiveness are similar: Warning-based approaches are shown to be effective in reducing the perceived accuracy of false headlines. Moreover, adding an explanation to the warning leads to the most significant results. In contrast, we could not find a significant effect on one of Facebook's current approaches (reduced post size and fact-checks in related articles).

Social media is gaining more and more importance in crisis management: “Social media continue to evolve, and so does their use in emergency and crisis events” (Reuter, Hughes, & Kaufhold, 2018). Re...

Since the terrorist attacks of 9/11, the use of social media in emergency and crisis events has greatly increased and many studies have concentrated on the use of ICT and social media before, during, or after these events. The field of research that these studies fall under is called crisis informatics. In this article, we evaluate and analyze crisis informatics research by looking at case studies of social media use in emergencies, outlining the types of research found in crisis informatics, and expounding upon the forms of interaction that have been researched. Finally, we summarize the achievements from a human–computer interaction perspective and outline trends and challenges for future research.

The value of social media in crises, disasters, and emergencies across different events (e.g. floods, storms, terroristic attacks), countries, and for heterogeneous participants (e.g. citizens, emergency services) is now well-attested. Existing work has examined the potentials and weaknesses of its use during specific events. Fewer studies, however, have focused on citizens' perceptions of social media in emergencies, and none have deployed a representative sample to examine this. We present the results of the first representative study on citizens' perception of social media in emergencies that we have conducted in Germany. Our study highlights, for example, that around half (45%) of people have used social media during an emergency to share and or look for information. In contrast, false rumours on social media (74%) are perceived as a threat. Moreover, only a minority of people have downloaded a smartphone app for emergencies (16%), with the most popular ones' weather and first aid apps.

Emergencies such as the 2013 Central European flood or the 2013 typhoon Haiyan in Philippines have shown how citizens can organize themselves and coordinate private relief activities. These activities can be found in (physical) groups of affected people, but also within (digital) social media communities. There is an evident need, however, for a clearer picture of what exactly is going on to be available for use by the official emergency services: to enlist them, to keep them safe, to support their efforts and to avoid needless duplications or conflicts. Aligning emergency services and volunteer activities is, then, crucial. In this paper we present a mobile crowd sensing based concept, which was designed as well as implemented as the application CrowdMonitor and facilitates the detection of physical and digital activities and the assignment of specific tasks to citizens. Finally we outline the findings of its evaluation.

Recent disasters have shown an increase in the significance of social media for both affected citizens and volunteers alike in the coordination of information and organization of relief activities, often independently of and in addition to the official emergency response. Existing research mainly focuses on the way in which individual platforms are used by volunteers in response to disasters. This paper examines the use of social media during the European Floods of 2013 and proposes a novel cross-social-media application for volunteers. Besides comprehensive analysis of volunteer communities, interviews were conducted with "digital volunteers" such as Facebook moderators of disaster-related groups. Based on the challenges identified, we designed and implemented the cross-social-media application "XHELP", which allows information to be both, acquired and distributed cross-media and cross-channel. The evaluation with 20 users leads to further design requirements for applications aiming to support volunteer moderators during disasters.

People all over the world are regularly affected by disasters and emergencies. Besides official emergency services, ordinary citizens are getting increasingly involved in crisis response work. They are usually present on-site at the place of incident and use social media to share information about the event. For emergency services, the large amount of citizen-generated content in social media, however, means that finding high-quality information is similar to “finding a needle in a haystack”. This article presents an approach to how a dynamic and subjective quality assessment of citizen-generated content could support the work of emergency services. First, we present results of our empirical study concerning the usage of citizen-generated content by emergency services. Based on our literature review and empirical study, we derive design guidelines and describe a concept for dynamic quality measurement that is implemented as a service-oriented web-application “Social Haystack.” Finally, we outline findings of its evaluation and implications thereof.

Emergencies are characterized by high complexity and unpredictability. In order to assess and manage them successfully, improvisation work and informal communication, even beyond local and organizational boundaries, is needed. Such informal practices can facilitate ad hoc participation of units in situation assessment, but this may lack overall situation awareness. This article presents a study on how emergent “collaboration needs” in current work of response teams located on-site and in the control center could be supported by mobile geo-collaboration systems. First, we present the results of an empirical study about informal work and mobile collaboration practices of emergency services. Then we describe the concept of a mobile geo-collaboration system that addresses the aspects detected in the empirical study and that was implemented as an Android application using web sockets, a technology enabling full-duplex ad hoc communication. Finally, we outline the findings of its evaluation in practice and its implications.

Improvisation is necessary when planned decision-making as the main managerial activity does not fit the conditions the practice provides. In these cases, information technology should not just automate planned and structured decisions, but support improvisational practice. In this contribution we present an empirical study about the improvisation work in scenarios of medium to large power outages in Germany. Our focus is on inter-organizational cooperation practices, thus we examined the cooperation of fire departments, police, public administration, electricity infrastructure operators and citizens. Our empirical material allows to describe reasons and conditions for improvisation. Our resulting recommendations address the support of aggregation and visualization of information, a necessary individualization of information compositions, options for collaborative situation assessment, requirements for informal and formal communication, and accessibility of information resources.

Adaptations of business processes are important in work environments, specifically when process-support needs to be tailored according to changing needs. The creation, management, and adaptation of the process models require typically modeling-experts. While these actors are knowledgeable in formalizing and operationalizing processes end-users who do not necessarily possess sophisticated modeling skills know typically local practices and framing conditions best. In this paper, we present an approach to support users in articulating their needs and to involve them into the (re-)design of process specifications. We explore how end-users reflect upon and articulate about business processes. Based on results of a qualitative study, we present a new, paper-based interaction technique, which enables users with little skills to model processes. The resulting process specifications can be transferred either in paper or in digital form into traditional modeling systems for further elaboration.