This paper describes semiotic inspection, a semiotic engineering evaluation method. It briefly identifies the essence of theory-based evaluation methods in HCI. Then it provides a detailed description and illustration of this method, which is based on a semiotic theory of HCI. It discusses its theoretical stance in semiotic engineering compared to the communicability evaluation method, as well as the perceived advantages and disadvantages of semiotic inspection. Finally, it points at the next steps in the semiotic inspection research agenda.

In this work we investigate the user practices in Instagram, a social photo sharing service. Some interesting conclusions emerge from our analysis. For instance, users tend to concentrate their posts during the weekend and at the end of the day. Furthermore, people tend to endorse photos with many likes and comments, inducing the rich get richer phenomenon. Our findings can support future research on sociology and cultural analytics research areas, such as on the proposal of new clustering algorithms based on the user practices in different social media networks.

Autism Spectrum Disorder (ASD) prevalence rates are increasing and serious games have shown a valuable potential to aid the treatment of autistic individuals. Hence, a Systematic Literature Review was conducted aiming at categorizing serious games for ASD children regarding which skills they aim to develop, how their activities were operationalized, and which customization options they provide to users. Our results showed that a large number of serious games aimed at developing distinct skills in ASD children have been proposed, with their main focus being on social and socio-emotional skills. Nonetheless, for each skill we characterize the existing games according to their features, that is the platform they are developed for, I/O devices used, required users’ action, audiovisual elements, and number of players. We also identify strategies adopted in the games regarding specific features and skills that emerged from the analysis. Finally, our results highlight that offering broader customization options in the games could expand their applicability and utility to their users. This work provides a thorough examination of research on games for ASD children, contributing both to researchers interested in the topic, by identifying existing contributions and open issues for research, and to professionals interested in developing serious games for this public.

Background: The potential of chatbots for screening and monitoring COVID-19 was envisioned since the outbreak of the disease. Chatbots can help disseminate up-to-date and trustworthy information, promote healthy social behavior, and support the provision of health care services safely and at scale. In this scenario and in view of its far-reaching postpandemic impact, it is important to evaluate user experience with this kind of application. Objective: We aimed to evaluate the quality of user experience with a COVID-19 chatbot designed by a large telehealth service in Brazil, focusing on the usability of real users and the exploration of strengths and shortcomings of the chatbot, as revealed in reports by participants in simulated scenarios. Methods: We examined a chatbot developed by a multidisciplinary team and used it as a component within the workflow of a local public health care service. The chatbot had 2 core functionalities: assisting web-based screening of COVID-19 symptom severity and providing evidence-based information to the population. From October 2020 to January 2021, we conducted a mixed methods approach and performed a 2-fold evaluation of user experience with our chatbot by following 2 methods: a posttask usability Likert-scale survey presented to all users after concluding their interaction with the bot and an interview with volunteer participants who engaged in a simulated interaction with the bot guided by the interviewer. Results: Usability assessment with 63 users revealed very good scores for chatbot usefulness (4.57), likelihood of being recommended (4.48), ease of use (4.44), and user satisfaction (4.38). Interviews with 15 volunteers provided insights into the strengths and shortcomings of our bot. Comments on the positive aspects and problems reported by users were analyzed in terms of recurrent themes. We identified 6 positive aspects and 15 issues organized in 2 categories: usability of the chatbot and health support offered by it, the former referring to usability of the chatbot and how users can interact with it and the latter referring to the chatbot’s goal in supporting people during the pandemic through the screening process and education to users through informative content. We found 6 themes accounting for what people liked most about our chatbot and why they found it useful—3 themes pertaining to the usability domain and 3 themes regarding health support. Our findings also identified 15 types of problems producing a negative impact on users—10 of them related to the usability of the chatbot and 5 related to the health support it provides. Conclusions: Our results indicate that users had an overall positive experience with the chatbot and found the health support relevant. Nonetheless, qualitative evaluation of the chatbot indicated challenges and directions to be pursued in improving not only our COVID-19 chatbot but also health chatbots in general.