Eliciting Empathy towards Urban Accessibility Issues

Empathy is an integral part of what it means to be human. Empathy refers to the ability to sense other people's emotions, coupled with the ability to imagine what they might be thinking and feeling. Architectural and urban design have identified empathy as a crucial factor in the design process and especially in user-centered participatory methods. Although empathy has been recognized as important for relating to other people's issues, current research has not explored how urban accessibility issues elicit empathy. We conducted a between-subjects online study where 202 participants observed five scenarios on different accessibility issues. Our results show that empathic traits and previous experience are significant factors in empathizing with accessibility issues. Additionally, storytelling and photos can influence perceptions of accessibility issues. The study highlights the importance of empathic traits and personal experience in understanding and addressing accessibility issues, as well as the potential of storytelling and photos in shaping perceptions of accessibility issues and evoking empathy. Our contribution demonstrates the advantages of incorporating narrative multimedia into design processes for improved urban accessibility.</p

What if we have Meta GPT? From Content Singularity to Human-Metaverse Interaction in AIGC Era

The global metaverse development is facing a "cooldown moment", while the academia and industry attention moves drastically from the Metaverse to AI Generated Content (AIGC) in 2023. Nonetheless, the current discussion rarely considers the connection between AIGCs and the Metaverse. We can imagine the Metaverse, i.e., immersive cyberspace, is the black void of space, and AIGCs can simultaneously offer content and facilitate diverse user needs. As such, this article argues that AIGCs can be a vital technological enabler for the Metaverse. The article first provides a retrospect of the major pitfall of the metaverse applications in 2022. Second, we discuss from a user-centric perspective how the metaverse development will accelerate with AIGCs. Next, the article conjectures future scenarios concatenating the Metaverse and AIGCs. Accordingly, we advocate for an AI-Generated Metaverse (AIGM) framework for energizing the creation of metaverse content in the AIGC era.Comment: 11 pages, 4 figure

Game of Words: Tagging Places through Crowdsourcing on Public Displays

ABSTRACT In this paper we present Game of Words, a crowdsourcing game for public displays that allows the creation of a keyword dictionary to describe locations. It relies on crowdsourcing and gamification to identify, filter, and rank keywords based on their relevance to the location of the public display itself. We demonstrate that crowdsourcing on public displays can leverage users&apos; knowledge of their environment, can work with a generic gaming task, and can be deployed on displays with multiple concurrent services. Our analysis shows that our approach has important benefits, such as the ability to identify undesired input, provide words of high semantic relevance, as well as a broader scope of keywords. Finally, our analysis also demonstrates that the chosen game design coped well with the challenges of this complex setting (i.e. public urban space) by disincentivising incorrect use of the system

Adult readers evaluating the credibility of social media posts: Prior belief consistency and source's expertise matter most

The present study investigates the role of source characteristics, the quality of evidence, and prior beliefs of the topic in adult readers' credibility evaluations of short health-related social media posts. The researchers designed content for the posts concerning five health topics by manipulating the source characteristics (source's expertise, gender, and ethnicity), the accuracy of the claims, and the quality of evidence (research evidence, testimony, consensus, and personal experience) of the posts. After this, accurate and inaccurate social media posts varying in the other manipulated aspects were programmatically generated. The crowdworkers (N = 844) recruited from two platforms were asked to evaluate the credibility of up to ten social media posts, resulting in 8380 evaluations. Before credibility evaluation, participants' prior beliefs on the topics of the posts were assessed. The results showed that prior belief consistency and the source's expertise affected the perceived credibility of the accurate and inaccurate social media posts the most after controlling for the topic of the post and the crowdworking platform. In contrast, the quality of evidence supporting the health claim mattered relatively little. The source's gender and ethnicity did not have any effect. The results are discussed in terms of first- and second-hand evaluation strategies.Comment: 16 pages, 4 figures including the appendix. Submitted to a journal for peer revie

Revisitation analysis of smartphone app use

We present a revisitation analysis of smartphone use to investigate the question: do smartphones induce usage habits? We analysed three months of application launch logs from 165 users in naturalistic settings. Our analysis reveals distinct clusters of applications and users which share similar revisitation patterns. However, we show that much of smartphone usage on a macro-level is very similar to web browsing on desktops, and thus argue that smartphone usage is driven by innate service needs rather than technology characteristics. On the other hand, on a micro-level we identify unique characteristics in smartphone usage, and we present a rudimentary model that accounts for 92 % in the variability of our smartphone use. Author Keywords Revisitation, smartphone use, habits, user behaviou

Correlating Pedestrian Flows and Search Engine Queries

An important challenge for ubiquitous computing is the development of techniques that can characterize a location vis-a-vis the richness and diversity of urban settings. In this paper we report our work on correlating urban pedestrian flows with Google search queries. Using longitudinal data we show pedestrian flows at particular locations can be correlated with the frequency of Google search terms that are semantically relevant to those locations. Our approach can identify relevant content, media, and advertisements for particular locations.Comment: 4 pages, 1 figure, 1 tabl

Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status : A longitudinal data analysis

Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious and incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential of in-situ quantification of human behaviour using mobile sensors to augment traditional methods of depression management. In this paper, we study whether self-reported mood scores and passive smartphone and wearable sensor data could be used to classify people as depressed or non-depressed. In a longitudinal study, our participants provided daily mood (valence and arousal) scores and collected data using their smartphones and Oura Rings. We computed daily aggregations of mood, sleep, physical activity, phone usage, and GPS mobility from raw data to study the differences between the depressed and non-depressed groups and created population-level Machine Learning classification models of depression. We found statistically significant differences in GPS mobility, phone usage, sleep, physical activity and mood between depressed and non-depressed groups. An XGBoost model with daily aggregations of mood and sensor data as predictors classified participants with an accuracy of 81.43% and an Area Under the Curve of 82.31%. A Support Vector Machine using only sensor-based predictors had an accuracy of 77.06% and an Area Under the Curve of 74.25%. Our results suggest that digital biomarkers are promising in differentiating people with and without depression symptoms. This study contributes to the body of evidence supporting the role of unobtrusive mobile sensor data in understanding depression and its potential to augment depression diagnosis and monitoring. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CCPeer reviewe