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Exploring the Association between Livestreamers’ Self-identified Gender and Their Viewers’ Linguistic Behavior
No full textThis study explores how the gender of streamers on the livestreaming platform Twitch is associated with their viewers’ linguistic behavior. Based on prior literature, we specifically examine how Twitch viewers’ use of two contrasting language types—polite words and swear words—varies by their streamer’s gender. We identified eight gender-diverse Twitch streamers who played a video game together and livestreamed their game play separately on their own channels. From these eight channels, 8,296 chat messages were collected and analyzed using automated text analysis. Our results showed that, based on self-identified gender, men streamers’ viewers employed more swear words than women streamers’ viewers. Meanwhile, women streamers’ viewers used more polite words than men streamers’ viewers. Additionally, we found that the use of both polite and swear words differs significantly between viewers of cisgender and non-binary streamers, which underscores the need for researchers to collect self-identified gender information in social media research
Machine Heuristic at Work: User Evaluations and Folk Theories of Weather News in ‘Immersive Mixed Reality’ Video
No full textImmersive mixed reality (IMR) technologies are transforming news reporting by enhancing audience engagement and improving the visualization of meteorological phenomena. While these technologies offer benefits for communicating public issues through news media, their complexity may pose challenges to audience understanding and trust in the information presented. This study adopted a mixed-methods approach to investigate how users evaluate news videos produced using IMR techniques compared to the traditional text-based format, and how individual differences in machine heuristics influence evaluations and folk theories. The immersive format is more effective in enhancing users’ sense of novelty and personal relevance. Machine heuristic significantly positively moderates the effect of news format on personal relevance. While a high machine heuristic is associated with positive perceptions and the data-driven nature of technology, low machine heuristic individuals form intuitive theories that emphasize AI involvement. Implications for the use and disclosure of immersive technology in news are discussed
Measurement Equivalence of the System Trustworthiness Scale Across Tasks
Full text linkThe System Trustworthiness Scale (STS) is a recent measure designed to capture trustworthiness perceptions. However, no tests of measurement equivalence (ME) have been conducted despite its importance for scale implementation. Measurement equivalence testing determines if the construct of interest is interpreted the same across groups, which is an important pre-requisite to examining group mean differences. The present work sought to investigate the measurement equivalence of the STS with multigroup confirmatory factor analyses across two different tasks to better understand the impact of context on system trustworthiness. The results provided evidence of ME, demonstrating the interpretability of the STS across contexts, providing additional evidence for the diverse use and interpretability of the STS as a measure of system trustworthiness. The authors implore future research to further examine potential measurement differences to better understand the generalizability of the STS
Disability Bias Detection in Electra-based Masked Language Model
No full textDisability bias is one of the most challenging sociodemographic bias to mitigate. To promote a more just and inclusive society, developers and researchers are strongly advised to create language models by prioritizing ethical considerations, where the benefits and opportunities of AI are accessible to all users and groups. The creation of new design guidelines and datasets are therefore essential to help AI systems realize their enormous potential for the benefit of people with disabilities. This paper presents a study on disability bias detection in Electra-Large-based Masked Language. Participatory research was conducted by involving three disability organizations, in collecting disability information for deaf and hard of hearing people. The results of our initial analysis significantly reveal the sensitivity of the model studied to the different identity references used to designate deaf and hard of hearing people
I Hate It (Smiley with Heart): Exploring Generation Z’s Perceived Socio-Psychological Motives, Attitudes, and Cultural Impact on Incongruent Emoji Use
Full text linkThis study examined Generation Z’s perceived socio-psychological motivations and attitudes toward incongruent emoji use, particularly focusing on cultural influences at both national and situational levels. Grounded in Uses and Gratifications Theory and the concept of language attitudes, the study employed a quasi-experimental design using social media-style stimuli where emoji congruence and emotional intensity were manipulated. Results indicated that incongruent emoji use was significantly associated with motivations including expressing uniqueness, belonging, humor, and saving face, while releasing emotions and seeking emotional support were more linked to congruent emojis, and was perceived as more creative but less appropriate and comprehensible. Overall, these findings suggest that Generation Z uses incongruent emojis as strategic tools for identity expression and self-presentation in public digital spaces. The study reveals that situational-level cultural orientations are more influential than national-level categories, reflecting cultural convergence in online environments
Open Value Creation as a Trust Signal in Government Procurement: Identifying GovTech Archetypes
Full text linkPublic procurement emphasizes fairness, accountability, transparency, and reusability. To meet these standards, tenders often involve complex awarding processes that pose challenges for non-established players. Small and medium-sized enterprises must therefore find ways to signal their capabilities, legitimacy, and ability to meet requirements—especially when competing with well-connected, established firms. Open Value Creation (OVC) offers a potential signaling mechanism. This study explores whether GovTech startups—technology ventures serving the public sector—use OVC to address these challenges. Based on 101 semi-structured interviews with GovTech representatives in Germany, we identify four OVC archetypes: (i) Believers, (ii) Pragmatists, (iii) Realists, and (iv) Refusers. Our findings show that OVC is widely seen as a way to build legitimacy, transparency, and trust with public clients, reduce information asymmetries, and improve access to reputation-driven procurement markets. Signaling strategies centered on OVC thus play a key role on the supply side of public procurement
Guardrail Vulnerabilities in Open-Source Language Models: Implications for Democratic Discourse and Marginalized Communities
Full text linkThe proliferation of open-source Large Language Models (LLMs) presents a complex technological phenomenon with significant societal implications. While these models democratize access to advanced Natural Language Processing (NLP) capabilities, they simultaneously amplify risks for marginalized communities who often bear the disproportionate burden of technological misuse. Our research examines systematic vulnerabilities in guardrail mechanisms across seven prominent open-source LLMs, revealing patterns of harmful content generation that threaten democratic discourse and social cohesion. Through empirical analysis using advanced NLP classification methods, we demonstrate that popular open-source models consistently generate content classified as hateful or offensive when subjected to adversarial prompting techniques. These findings directly contradict the safety assurances provided by model developers, particularly Meta AI's stated commitment that their systems should present balanced perspectives on debated policy issues rather than singular viewpoints
The Influence of Institutional Trust on the Adoption of the Digital Euro
Full text linkAs the digital euro advances through its preparatory phase, the success of this novel means of payment hinges on the attitude of end-users towards its adoption and related trust-building mechanisms. Yet, research is scarce on trust in the context of central bank digital currencies (CBDCs) from an end-user’s perspective. Drawing on institutional trust theory, this work investigates the influence of structural assurances – policy and technology assurance – on individuals’ willingness to adopt a digital euro. A survey conducted among 421 participants from France, Germany, and Italy highlights the central role of institutional trust as a key component of trust in the digital euro system, influencing end-users’ attitudes toward its adoption. The empirical findings contribute to the evolving literature on retail CBDCs through the end-user’s lens and offer practical guidance for central banks and policymakers regarding the key role of both policy and technology assurance
Climbing Knowledge Hills: Systemic Thinking and the Topography of Lucky Discovery
Full text linkThis conceptual paper explores the role of systemic thinking and luck in scientific collaboration. Innovative ideas often emerge from interdisciplinarity, yet increasing specialization in science may have a negative effect on the knowledge transfer across different domains. We propose that familiarity with complex systems theory —- a universal conceptual framework -- may improve the likelihood of generating novel interdisciplinary insights and reduce knowledge friction. To illustrate this mechanism, we apply the concepts of energy landscapes and critical transitions. Selected principles of complex systems are presented to illustrate their cross-domain applicability. We conclude that adopting a systemic perspective can optimize the knowledge network between otherwise cognitively distant scientific communities, thereby increasing the probability of serendipitous and innovative knowledge combinations
A Framework for Advancing Autonomy in Space Robotics: Towards Self-Sustaining Exploration
Full text linkAs humanity embarks on deeper space exploration, the integration of robotic autonomy presents transformative potential. % for overcoming challenges associated with exploration, habitat construction, and resource extraction. This paper explores the dynamics of Human-Robot Interaction (HRI), focusing on how autonomous robotic systems engage with unstructured and extreme environments. We examine the role of robotic autonomy, where intelligent decision-making enables exploration, habitat construction, and resource extraction, drawing parallels between human-led and robot-driven space missions. In doing so we systematically investigate the potential for autonomous systems to operate independently in high-risk environments. Using the Autonomy Levels for Unmanned Systems (ALFUS) framework, we assess planetary robots' autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and Technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy. Based on this established methodology, we introduce the Autonomous Robotics for Planetary Exploration (ARPE), a novel conceptual framework defining the roles of robotic agents involved, to connect theoretical insights with real-world applications. This work highlights the significance of autonomous decision-making in planetary exploration to enhance mission success and proposes directions for future research on long-term robotic sustainability in extreme environments