TY - JOUR A1 - Schachner, Theresa A1 - Gross, Christoph A1 - Hasl, Andrea A1 - Wangenheim, Florian von A1 - Kowatsch, Tobias T1 - Deliberative and paternalistic interaction styles for conversational agents in digital health BT - procedure and validation through a web-based experiment JF - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR N2 - Background: Recent years have witnessed a constant increase in the number of people with chronic conditions requiring ongoing medical support in their everyday lives. However, global health systems are not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. Moreover, different aspects of CAs have not yet been sufficiently investigated to fully exploit their potential. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the interaction between patients and physicians. Patient-physician interaction is recognized as a critical success factor for patient satisfaction, treatment adherence, and subsequent treatment outcomes. However, so far, it remains effectively unknown how different interaction styles can be implemented into CA interactions and whether these styles are recognizable by users. Objective: The objective of this study was to develop an approach to reproducibly induce 2 specific interaction styles into CA-patient dialogs and subsequently test and validate them in a chronic health care context. Methods: On the basis of the Roter Interaction Analysis System and iterative evaluations by scientific experts and medical health care professionals, we identified 10 communication components that characterize the 2 developed interaction styles: deliberative and paternalistic interaction styles. These communication components were used to develop 2 CA variations, each representing one of the 2 interaction styles. We assessed them in a web-based between-subject experiment. The participants were asked to put themselves in the position of a patient with chronic obstructive pulmonary disease. These participants were randomly assigned to interact with one of the 2 CAs and subsequently asked to identify the respective interaction style. Chi-square test was used to assess the correct identification of the CA-patient interaction style. Results: A total of 88 individuals (42/88, 48% female; mean age 31.5 years, SD 10.1 years) fulfilled the inclusion criteria and participated in the web-based experiment. The participants in both the paternalistic and deliberative conditions correctly identified the underlying interaction styles of the CAs in more than 80% of the assessments (X-1(,8)8(2)=38.2; P<.001; phi coefficient r(phi)=0.68). The validation of the procedure was hence successful. Conclusions: We developed an approach that is tailored for a medical context to induce a paternalistic and deliberative interaction style into a written interaction between a patient and a CA. We successfully tested and validated the procedure in a web-based experiment involving 88 participants. Future research should implement and test this approach among actual patients with chronic diseases and compare the results in different medical conditions. This approach can further be used as a starting point to develop dynamic CAs that adapt their interaction styles to their users. KW - conversational agents KW - chatbots KW - human-computer interaction KW - physician-patient relationship KW - interaction styles KW - deliberative KW - interaction KW - paternalistic interaction KW - digital health KW - chronic KW - conditions KW - COPD Y1 - 2021 U6 - https://doi.org/10.2196/22919 SN - 1438-8871 VL - 23 IS - 1 PB - Healthcare World CY - Richmond, Va. ER - TY - JOUR A1 - Kaufhold, Marc-André A1 - Bayer, Markus A1 - Bäumler, Julian A1 - Reuter, Christian A1 - Stieglitz, Stefan A1 - Basyurt, Ali Sercan A1 - Mirbabaie, Milad A1 - Fuchss, Christoph A1 - Eyilmez, Kaan T1 - CYLENCE: strategies and tools for cross-media reporting, detection, and treatment of cyberbullying and hatespeech in law enforcement agencies JF - Mensch und Computer 2023: Workshopband MuC 2023 N2 - Despite the merits of public and social media in private and professional spaces, citizens and professionals are increasingly exposed to cyberabuse, such as cyberbullying and hate speech. Thus, Law Enforcement Agencies (LEA) are deployed in many countries and organisations to enhance the preventive and reactive capabilities against cyberabuse. However, their tasks are getting more complex by the increasing amount and varying quality of information disseminated into public channels. Adopting the perspectives of Crisis Informatics and safety-critical Human-Computer Interaction (HCI) and based on both a narrative literature review and group discussions, this paper first outlines the research agenda of the CYLENCE project, which seeks to design strategies and tools for cross-media reporting, detection, and treatment of cyberbullying and hatespeech in investigative and law enforcement agencies. Second, it identifies and elaborates seven research challenges with regard to the monitoring, analysis and communication of cyberabuse in LEAs, which serve as a starting point for in-depth research within the project. KW - cyberbullying KW - hate speech KW - law enforcement agencies KW - situational awareness KW - human-computer interaction Y1 - 2023 UR - https://dl.gi.de/handle/20.500.12116/42064 U6 - https://doi.org/10.18420/MUC2023-MCI-WS01-211 SP - 1 EP - 8 PB - Gesellschaft für Informatik e.V. (GI) CY - Bonn ER - TY - GEN A1 - Perlich, Anja A1 - Meinel, Christoph T1 - Cooperative Note-Taking in Psychotherapy Sessions BT - an evaluation of the T2 - 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) N2 - In the course of patient treatments, psychotherapists aim to meet the challenges of being both a trusted, knowledgeable conversation partner and a diligent documentalist. We are developing the digital whiteboard system Tele-Board MED (TBM), which allows the therapist to take digital notes during the session together with the patient. This study investigates what therapists are experiencing when they document with TBM in patient sessions for the first time and whether this documentation saves them time when writing official clinical documents. As the core of this study, we conducted four anamnesis session dialogues with behavior psychotherapists and volunteers acting in the role of patients. Following a mixed-method approach, the data collection and analysis involved self-reported emotion samples, user experience curves and questionnaires. We found that even in the very first patient session with TBM, therapists come to feel comfortable, develop a positive feeling and can concentrate on the patient. Regarding administrative documentation tasks, we found with the TBM report generation feature the therapists save 60% of the time they normally spend on writing case reports to the health insurance. KW - user experience KW - emotion measurement KW - computer-mediated therapy KW - behavior psychotherapy KW - human-computer interaction KW - medical documentation KW - note-taking Y1 - 2018 SN - 978-1-5386-4294-8 PB - IEEE CY - New York ER - TY - THES A1 - Garoufi, Konstantina T1 - Interactive generation of effective discourse in situated context : a planning-based approach T1 - Interaktive Generierung von effektivem Diskurs in situiertem Kontext: Ein planungsbasierter Ansatz N2 - As our modern-built structures are becoming increasingly complex, carrying out basic tasks such as identifying points or objects of interest in our surroundings can consume considerable time and cognitive resources. In this thesis, we present a computational approach to converting contextual information about a person's physical environment into natural language, with the aim of helping this person identify given task-related entities in their environment. Using efficient methods from automated planning - the field of artificial intelligence concerned with finding courses of action that can achieve a goal -, we generate discourse that interactively guides a hearer through completing their task. Our approach addresses the challenges of controlling, adapting to, and monitoring the situated context. To this end, we develop a natural language generation system that plans how to manipulate the non-linguistic context of a scene in order to make it more favorable for references to task-related objects. This strategy distributes a hearer's cognitive load of interpreting a reference over multiple utterances rather than one long referring expression. Further, to optimize the system's linguistic choices in a given context, we learn how to distinguish speaker behavior according to its helpfulness to hearers in a certain situation, and we model the behavior of human speakers that has been proven helpful. The resulting system combines symbolic with statistical reasoning, and tackles the problem of making non-trivial referential choices in rich context. Finally, we complement our approach with a mechanism for preventing potential misunderstandings after a reference has been generated. Employing remote eye-tracking technology, we monitor the hearer's gaze and find that it provides a reliable index of online referential understanding, even in dynamically changing scenes. We thus present a system that exploits hearer gaze to generate rapid feedback on a per-utterance basis, further enhancing its effectiveness. Though we evaluate our approach in virtual environments, the efficiency of our planning-based model suggests that this work could be a step towards effective conversational human-computer interaction situated in the real world. N2 - Die zunehmende Komplexität moderner Gebäude und Infrastrukturen führt dazu, dass alltägliche Aktivitäten, wie z.B. die Identifizierung von gesuchten Objekten in unserer Umgebung und das Auffinden von Orten, beträchtliche Zeit und kognitive Ressourcen in Anspruch nehmen können. In dieser Dissertation werden computerbasierte Verfahren präsentiert, welche eine Person dabei unterstützen, Zielobjekte in Ihrem Umfeld zu identifizieren. Dabei werden Informationen über die Situation und das physische Umfeld der Person - der sog. situierte Kontext - in natürliche Sprache umgewandelt. So wird Diskurs generiert, der einen Hörer interaktiv zum Erreichen eines Zieles bzw. zum Abschließen einer Aufgabe führt. Hierbei kommen Methoden aus der Planung zum Einsatz, einem Gebiet der künstlichen Intelligenz, welches sich mit der Berechnung von zielgerichteten Handlungsabfolgen beschäftigt. Die in dieser Arbeit vorgestellten Verfahren widmen sich den Herausforderungen der Kontrolle des situierten Kontexts, der Anpassung an den situierten Kontext sowie der Überwachung des situierten Kontexts. Zu diesem Zweck wird zunächst ein Sprachgenerierungssystem entwickelt, das plant, wie der nicht-linguistische Kontext einer Szene manipuliert werden kann, damit die Referenz auf relevante Objekte erleichtert wird. Dadurch ist es möglich, die kognitive Beanspruchung eines Hörers bei der Interpretation einer Referenz über mehrere sprachliche Äußerungen zu verteilen. Damit die linguistischen Entscheidungen des Systems in einem vorgegebenen Kontext optimiert werden können, wird weiterhin gelernt, die Äußerungen von Sprechern danach zu differenzieren, wie hilfreich sie in bestimmten Situationen für die Hörer waren. Dabei wird das Verhalten von menschlichen Sprechern, welches sich als hilfreich erwiesen hat, modelliert. Das daraus entstehende System kombiniert symbolisches und statistisches Schließen und stellt somit einen Lösungsansatz für das Problem dar, wie nicht-triviale referentielle Entscheidungen in reichem Kontext getroffen werden können. Zum Schluss wird ein komplementärer Mechanismus vorgestellt, der potentielle Missverständnisse bzgl. generierter Referenzen verhindern kann. Zu diesem Zweck kommt Blickerfassungstechnologie zum Einsatz. Auf Basis der Überwachung und Auswertung des Blicks des Hörers können Rückschlüsse über die Interpretation gegebener Referenzen gemacht werden; dieser Mechanismus funktioniert auch in sich dynamisch verändernden Szenen zuverlässig. Somit wird ein System präsentiert, welches den Blick des Hörers nutzt, um rasch Feedback zu generieren. Dieses Vorgehen verbessert die Effektivität des Diskurses zusätzlich. Die vorgestellten Verfahren werden in virtuellen Umwelten evaluiert. Die Effizienz des planungsbasierten Modells ist allerdings ein Indiz dafür, dass die in dieser Arbeit gemachten Vorschläge dazu dienen können, effektive Mensch-Computer-Interaktion auf Basis von Sprache auch in der realen Welt umzusetzen. KW - natural language generation KW - human-computer interaction KW - situated context KW - effective discourse KW - automated planning Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-69108 ER -