151 research outputs found

    Tell me, what are you most afraid of? Exploring the Effects of Agent Representation on Information Disclosure in Human-Chatbot Interaction

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    Self-disclosure counts as a key factor influencing successful health treatment, particularly when it comes to building a functioning patient-therapist-connection. To this end, the use of chatbots may be considered a promising puzzle piece that helps foster respective information provision. Several studies have shown that people disclose more information when they are interacting with a chatbot than when they are interacting with another human being. If and how the chatbot is embodied, however, seems to play an important role influencing the extent to which information is disclosed. Here, research shows that people disclose less if the chatbot is embodied with a human avatar in comparison to a chatbot without embodiment. Still, there is only little information available as to whether it is the embodiment with a human face that inhibits disclosure, or whether any type of face will reduce the amount of shared information. The study presented in this paper thus aims to investigate how the type of chatbot embodiment influences self-disclosure in human-chatbot-interaction. We conducted a quasi-experimental study in which n=178n=178 participants were asked to interact with one of three settings of a chatbot app. In each setting, the humanness of the chatbot embodiment was different (i.e., human vs. robot vs. disembodied). A subsequent discourse analysis explored difference in the breadth and depth of self-disclosure. Results show that non-human embodiment seems to have little effect on self-disclosure. Yet, our data also shows, that, contradicting to previous work, human embodiment may have a positive effect on the breadth and depth of self-disclosure.Comment: 13 page

    “You drowned me in tears, where did you go?” Narratives of Reproductive Loss and Grief in Middle-Class India

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    This study is an analysis of middle-class couples’ experiences of reproductive loss, the ensuing grief, and their relentless struggles in order to achieve reproductive success in Kolkata, India. Based on ethnographic engagements, the study explains how the increasingly biomedicalised setting and middle-class ethos of 21st century, urban India shape such profoundly disruptive reproductive experiences. In doing so, the study illustrates how the couple’s experiences of loss and grief were constituted by multiple and intricately entangled enactments of gender roles, gendered emotions, entities, and normative concepts. Finally, the study pays attention to the processual utilisation of constrained agency by the actors, particularly, by the women, in order to show how they coped with their loss as well as their disrupted conjugal lives

    Affective reactions towards socially interactive agents and their computational modeling

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    Over the past 30 years, researchers have studied human reactions towards machines applying the Computers Are Social Actors paradigm, which contrasts reactions towards computers with reactions towards humans. The last 30 years have also seen improvements in technology that have led to tremendous changes in computer interfaces and the development of Socially Interactive Agents. This raises the question of how humans react to Socially Interactive Agents. To answer these questions, knowledge from several disciplines is required, which is why this interdisciplinary dissertation is positioned within psychology and computer science. It aims to investigate affective reactions to Socially Interactive Agents and how these can be modeled computationally. Therefore, after a general introduction and background, this thesis first provides an overview of the Socially Interactive Agent system used in this work. Second, it presents a study comparing a human and a virtual job interviewer, which shows that both interviewers induce shame in participants to the same extent. Thirdly, it reports on a study investigating obedience towards Socially Interactive Agents. The results indicate that participants obey human and virtual instructors in similar ways. Furthermore, both types of instructors evoke feelings of stress and shame to the same extent. Fourth, a stress management training using biofeedback with a Socially Interactive Agent is presented. The study shows that a virtual trainer can teach coping techniques for emotionally challenging social situations. Fifth, it introduces MARSSI, a computational model of user affect. The evaluation of the model shows that it is possible to relate sequences of social signals to affective reactions, taking into account emotion regulation processes. Finally, the Deep method is proposed as a starting point for deeper computational modeling of internal emotions. The method combines social signals, verbalized introspection information, context information, and theory-driven knowledge. An exemplary application to the emotion shame and a schematic dynamic Bayesian network for its modeling are illustrated. Overall, this thesis provides evidence that human reactions towards Socially Interactive Agents are very similar to those towards humans, and that it is possible to model these reactions computationally.In den letzten 30 Jahren haben Forschende menschliche Reaktionen auf Maschinen untersucht und dabei das “Computer sind soziale Akteure”-Paradigma genutzt, in dem Reaktionen auf Computer mit denen auf Menschen verglichen werden. In den letzten 30 Jahren hat sich ebenfalls die Technologie weiterentwickelt, was zu einer enormen VerĂ€nderung der Computerschnittstellen und der Entwicklung von sozial interaktiven Agenten gefĂŒhrt hat. Dies wirft Fragen zu menschlichen Reaktionen auf sozial interaktive Agenten auf. Um diese Fragen zu beantworten, ist Wissen aus mehreren Disziplinen erforderlich, weshalb diese interdisziplinĂ€re Dissertation innerhalb der Psychologie und Informatik angesiedelt ist. Sie zielt darauf ab, affektive Reaktionen auf sozial interaktive Agenten zu untersuchen und zu erforschen, wie diese computational modelliert werden können. Nach einer allgemeinen EinfĂŒhrung in das Thema gibt diese Arbeit daher, erstens, einen Überblick ĂŒber das Agentensystem, das in der Arbeit verwendet wird. Zweitens wird eine Studie vorgestellt, in der eine menschliche und eine virtuelle Jobinterviewerin miteinander verglichen werden, wobei sich zeigt, dass beide Interviewerinnen bei den Versuchsteilnehmenden SchamgefĂŒhle in gleichem Maße auslösen. Drittens wird eine Studie berichtet, in der Gehorsam gegenĂŒber sozial interaktiven Agenten untersucht wird. Die Ergebnisse deuten darauf hin, dass Versuchsteilnehmende sowohl menschlichen als auch virtuellen Anleiterinnen Ă€hnlich gehorchen. DarĂŒber hinaus werden durch beide Instruktorinnen gleiche Maße von Stress und Scham hervorgerufen. Viertens wird ein Biofeedback-Stressmanagementtraining mit einer sozial interaktiven Agentin vorgestellt. Die Studie zeigt, dass die virtuelle Trainerin Techniken zur BewĂ€ltigung von emotional herausfordernden sozialen Situationen vermitteln kann. FĂŒnftens wird MARSSI, ein computergestĂŒtztes Modell des Nutzeraffekts, vorgestellt. Die Evaluation des Modells zeigt, dass es möglich ist, Sequenzen von sozialen Signalen mit affektiven Reaktionen unter BerĂŒcksichtigung von Emotionsregulationsprozessen in Beziehung zu setzen. Als letztes wird die Deep-Methode als Ausgangspunkt fĂŒr eine tiefer gehende computergestĂŒtzte Modellierung von internen Emotionen vorgestellt. Die Methode kombiniert soziale Signale, verbalisierte Introspektion, Kontextinformationen und theoriegeleitetes Wissen. Eine beispielhafte Anwendung auf die Emotion Scham und ein schematisches dynamisches Bayes’sches Netz zu deren Modellierung werden dargestellt. Insgesamt liefert diese Arbeit Hinweise darauf, dass menschliche Reaktionen auf sozial interaktive Agenten den Reaktionen auf Menschen sehr Ă€hnlich sind und dass es möglich ist diese menschlichen Reaktion computational zu modellieren.Deutsche Forschungsgesellschaf

    International Academic Symposium of Social Science 2022

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    This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate

    Real-time generation and adaptation of social companion robot behaviors

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    Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukĂŒnftigen Zuhauses sein. Sie werden uns bei alltĂ€glichen Aufgaben unterstĂŒtzen, uns unterhalten und uns mit hilfreichen RatschlĂ€gen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunĂ€chst ĂŒberwunden werden mĂŒssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche FĂ€higkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natĂŒrliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. DarĂŒber hinaus mĂŒssen Roboter auch die individuellen Vorlieben der Benutzer berĂŒcksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprĂ€gt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter fĂŒhrt. Roboter haben jedoch keine menschliche Intuition - sie mĂŒssen mit entsprechenden Algorithmen fĂŒr diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestĂ€rkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die BedĂŒrfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle ĂŒber die multimodale Verhaltenserzeugung des Roboters, ein VerstĂ€ndnis des menschlichen Feedbacks und eine algorithmische Basis fĂŒr maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen fĂŒr die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestĂ€rkendem Lernen entwickelt. Er bietet eine ĂŒbergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird fĂŒr mehrere AnwendungsfĂ€lle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen fĂŒhrt, die in Labor- und In-situ-Studien evaluiert werden. Diese AnsĂ€tze befassen sich mit typischen AktivitĂ€ten in hĂ€uslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an

    Who is ‘We’ in ‘We, the future without future?’ On generational identity and youth (digital) activism in and beyond FridaysForFuture-Rome

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    The average global temperatures spiked in the last century and extreme climate phenomena became increasingly and dramatically common. The conditions of the planet have contributed to inaugurating a novel wave of climate activism, which sees an important contribution in young people, who are mobilizing worldwide to ask for better policies to face what has been defined as humanity’s greatest challenge. The FridaysForFuture (FFF for short) movement has especially been at the forefront of this fight. Inspired by Greta Thunberg’s 2018 Friday school strikes in front of the Swedish Parliament, the movement has spread globally in a complex network of national and local groups that share common values and goals (inclusivity, intersectionality, decarbonization...) but also express their unique geographical and cultural identity as they localize the climate fight to each group’s necessities. As it is already clear from the name ‘FridaysForFuture,’ the movement’s fight is strongly connected to the generational identity and youth-based sense-makings of its members. Incipient literature on FFF has observed how especially young activists join the movement to safeguard the interests of their own generation, following the idea that older generations have doomed the planet and taken the future away from younger people. In this context, social media are privileged platforms for FFF activists, who resort to them for advocacy and awareness-raising, while also recruiting adhesions to the movement in a continuous hybridization of meanings and practices that blurs the boundaries between online and offline spaces. FFF-activists’ social media usage practices are also informed by younger people’s media ideologies (Gershon 2010b) and sense-makings and can therefore open windows in the unique ways young people understand social media as environments for both digital activism and generational identity-building processes. Informed by literature addressing identity making practices, collective identity, generational ‘we sense,’ digital and youth activism, this thesis investigates the interplay between generational identity and youth social media activism focusing on the FFF group of FFF-Rome. This study is a multimethod qualitative research, combining a six-month multimodal ethnography (of the group’s activities and its Instagram page) and semi-structured interviews to FFF-Rome activists. Consistent with an ecological approach to social media, this method allowed for the direct observation of social actions as they happened, preventing a disjunction between their contexts and individual and collective meanings. These choices were complementary with the adoption of innovative ethical standards and practices of engaged research. As a result, this thesis advocates for ‘committed’ research when studying social movements, favoring research appropriation by the activists and in solidarity with their fight. Concretely, this work answers the following research questions: 1. How do FFF-Rome activists combine their generational identity with being climate activists? 2. What can the case of FFF-Rome tell us about the current generation of youth (climate) activists and, more in general, about the identity of this generation of young people? 3. How do social media usage practices and FFF-Rome’s identity mutually shape each other? 4. How do FFF-Rome activists negotiate social media usage practices and norms within the movement? Part 1 addresses RQ 1 and 2 by observing how the activists combine a generational understanding of climate activism and climate change with their own identity as young people of the 21st century. Part 2 answers RQs 3 and 4 by analyzing how the activists appropriate digital platforms as youth’s ‘own’ channels, and how they move seamlessly between online and offline environments, negotiating architectural and technical affordances. While different parts of this thesis answer distinct research questions, all sections are strongly interconnected and contribute to all research questions collectively. The conclusions especially highlight this bond and suggest that changes in the communicative infrastructures have essentially redefined the communicative and political practices of climate activism. It is not just the struggle that is generationally connoted, but also the communicative channels and the protest practices that accompany it. FFF-Rome activists fully legitimize digital activism and incorporate it in all phases of their struggle, intertwining social media ideologies (Gershon 2010b) with activist ideologies. In this context, social media are considered both as a means to an end and as digital spaces young people ‘own’ in virtue of their being young

    Human-Machine Communication: Complete Volume. Volume 6

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    his is the complete volume of HMC Volume 6

    Interpersonal Style Predicts Behavioral Heterogeneity During Economic-Exchange Task Gameplay in Individuals With Social Anxiety

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    Recent evidence suggests that individuals who exhibit socially anxious (SA) symptoms endorse patterns of maladaptive interpersonal behavior that can be parceled into three subtypes based upon interpersonal circumplex theory: friendly-submissive, hostile-submissive, and hostile-dominant. It remains unclear, however, whether these subtypes translate into observable social behavior in laboratory contexts. I used two economic-exchange tasks, the prisoner’s dilemma game (PDG) and the ultimatum game (UG), as models of domains of social behavior to detect interpersonal differences in a sample of college students (N= 88) who endorsed mild-to-severe levels of SA based upon responses to the Liebowitz Social Anxiety Scale Self-Report (LSAS-SR). Using a two-step automatic clustering procedure, the sample was divided into three groups according to their responses on the Inventory of Interpersonal Problems – 32 (IIP-32). Interpersonal profiles were constructed for these groups and two of the three expected subtypes were identified (friendly-submissive and hostile-submissive); however, instead of hostile-dominance, friendly-dominance emerged as a potential subtype. Hierarchical and quantile regressions were conducted to examine whether SA severity and interpersonal subtype predicted cooperation and acceptance rates in the PDG and UG respectively. The data revealed that in the PDG, SA severity significantly predicted an increase in cooperation rate, while the interpersonal subtypes did not have a significant effect. However, when analyses included only those individuals who met a clinical cutoff for severe SA (N = 66), SA severity no longer predicted cooperation rates. But friendly-submissiveness predicted cooperation rates exceeding 65% during gameplay, while friendly-dominance predicted a ceiling cooperation rate of 65%. Hostile-submissiveness did not predict variance in cooperation rate. In the UG, the interpersonal subtypes and SA severity did not significantly predict acceptance rate. These findings build upon a burgeoning literature substantiating links between self-reported interpersonal problems and unique interindividual psychopathological presentations. However, improvements in sample recruitment, the implementation of economic-exchange tasks, and data-analytic methods need to be put into practice before stronger assertions can be made concerning the therapeutic relevance of these games as social decision-making paradigms

    Anonymous Panda: preserving anonymity and expressiveness in online mental health platforms

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    Digital solutions that allow people to seek treatment, such as online psychological interventions and other technology-mediated therapies, have been developed to assist individuals with mental health disorders. Such approaches may raise privacy concerns about the use of people’s data and the safety of their mental health information. This work uses cutting-edge computer graphics technology to develop a novel system capable of increasing anonymity while maintaining expressiveness in computer-mediated mental health interventions. According to our preliminary findings, we were able to customize a realistic avatar using Live Link, Metahumans, and Unreal Engine 4 (UE4) with the same emotional depth as a real person. Furthermore, these findings showed that the virtual avatars’ inability to express themselves through hand motion gave the impression that they were acting in an unnatural way. By including the hand tracking feature using the Leap Motion Controller, we were able to improve our comprehension of the prospective use of ultra-realistic virtual human avatars in video conferencing therapy, i.e., both studies helped us understand how vital facial and body expressions are and how problematic their absence is in communicating with others.SoluçÔes digitais que permitem Ă s pessoas procurar tratamento, tais como terapias psicolĂłgicas online e outras terapias com recurso Ă  tecnologia, foram desenvolvidas para ajudar indivĂ­duos com distĂșrbios de saĂșde mental. Tais abordagens podem suscitar preocupaçÔes sobre a privacidade na utilização dos dados das pessoas e a segurança da informação sobre a sua saĂșde mental. Este trabalho utiliza tecnologia de ponta em computação grĂĄfica para desenvolver um sistema inovador capaz de aumentar o anonimato, mantendo simultaneamente a expressividade nas inter vençÔes de saĂșde mental mediadas por computador. Segundo os nossos resultados preliminares, conseguimos personalizar um avatar realista usando Live Link, Metahumans, e Unreal Engine 4 (UE4) com a mesma profundidade emocional que uma pessoa real. AlĂ©m disso, os resultados mostraram que a incapacidade dos avatares virtuais de se expressarem atravĂ©s do movimento das mĂŁos deu a impressĂŁo de que estavam a agir de uma forma pouco natural. Ao incluir a função de rastreio das mĂŁos utilizando o Leap Motion Controller, conseguimos melhorar a nossa compreensĂŁo do uso prospetivo de avatares humanos virtuais e ultrarrealistas na terapia de videoconferĂȘncia, ou seja, os estudos realizados ajudaram-nos a compreender como as expressĂ”es faciais e corporais sĂŁo vitais e como a sua ausĂȘncia Ă© problemĂĄtica na comunicação com os outros
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