@article{KaufholdBayerBaeumleretal.2023, author = {Kaufhold, Marc-Andr{\´e} and Bayer, Markus and B{\"a}umler, Julian and Reuter, Christian and Stieglitz, Stefan and Basyurt, Ali Sercan and Mirbabaie, Milad and Fuchss, Christoph and Eyilmez, Kaan}, title = {CYLENCE: strategies and tools for cross-media reporting, detection, and treatment of cyberbullying and hatespeech in law enforcement agencies}, series = {Mensch und Computer 2023: Workshopband MuC 2023}, journal = {Mensch und Computer 2023: Workshopband MuC 2023}, publisher = {Gesellschaft f{\"u}r Informatik e.V. (GI)}, address = {Bonn}, doi = {10.18420/MUC2023-MCI-WS01-211}, pages = {1 -- 8}, year = {2023}, abstract = {Despite the merits of public and social media in private and professional spaces, citizens and professionals are increasingly exposed to cyberabuse, such as cyberbullying and hate speech. Thus, Law Enforcement Agencies (LEA) are deployed in many countries and organisations to enhance the preventive and reactive capabilities against cyberabuse. However, their tasks are getting more complex by the increasing amount and varying quality of information disseminated into public channels. Adopting the perspectives of Crisis Informatics and safety-critical Human-Computer Interaction (HCI) and based on both a narrative literature review and group discussions, this paper first outlines the research agenda of the CYLENCE project, which seeks to design strategies and tools for cross-media reporting, detection, and treatment of cyberbullying and hatespeech in investigative and law enforcement agencies. Second, it identifies and elaborates seven research challenges with regard to the monitoring, analysis and communication of cyberabuse in LEAs, which serve as a starting point for in-depth research within the project.}, language = {en} } @article{SchachnerGrossHasletal.2021, author = {Schachner, Theresa and Gross, Christoph and Hasl, Andrea and Wangenheim, Florian von and Kowatsch, Tobias}, title = {Deliberative and paternalistic interaction styles for conversational agents in digital health}, series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, volume = {23}, journal = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, number = {1}, publisher = {Healthcare World}, address = {Richmond, Va.}, issn = {1438-8871}, doi = {10.2196/22919}, pages = {13}, year = {2021}, abstract = {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.}, language = {en} } @misc{PerlichMeinel2018, author = {Perlich, Anja and Meinel, Christoph}, title = {Cooperative Note-Taking in Psychotherapy Sessions}, series = {2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)}, journal = {2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-4294-8}, pages = {6}, year = {2018}, abstract = {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.}, language = {en} } @phdthesis{Garoufi2013, author = {Garoufi, Konstantina}, title = {Interactive generation of effective discourse in situated context : a planning-based approach}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69108}, school = {Universit{\"a}t Potsdam}, year = {2013}, abstract = {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.}, language = {en} }