My guiding question is, “how we can I use storytelling to empower the stories and experiences of the black community in the Central Area that has been displaced through gentrification?” Redlining has historically made it harder for people of color to obtain a mortgage or buy a property in certain neighborhoods, and it continues to exist as gentrification. As tech companies like Microsoft, Amazon, and Airbnb among others continue to expand their presence in the greater Seattle area, rent prices have risen and property owners have started building expensive apartments and living complexes for the influx of workers with a high income. This expansion has displaced people of color who have historically lived in the Central Area. Currently there are stories of resilience in communities that have been displaced by gentrification, but they are often not showcased in the public eye and media outlets. I want to focus on the use of radical storytelling as an act of defiance against erasure, and the role of tech companies in the greater Seattle area in displacing communities. Inspired by the interactive digital stories like “Trump Wants a Border Wall. See What’s In Place Already” in the New York Times or “Microsoft By the Numbers” on Microsoft Story Labs, I hope to create a visual digital story about the ways and means of gentrification, and I hope this story can be used to educate the public about the mechanisms of gentrification and where communities move. By conducting interviews with activists and community members in the Central Area and highlighting their perspective through direct quotes and testimonials, I hope to educate everyone about the strength of communities in the Central Area. My goal is to create a project that focuses on empowering people of color and their stories through community activation.

Have you ever had to motivate the team? What was the result? The English language Wikipedia is notable for its large number of articles. However, 288 other active language editions of Wikipedia have also developed through the intricate interactions of contributing editors. While the editor interactions in the English Wikipedia have been researched extensively, these other language editions remain understudied. To understand how editors currently come to consensus in article building in the French language, a team of researchers has leveraged an existing English framework that depicts how power and policies play a role in mass collaboration. Using this English language framework, we are using qualitative coding methods to build a unique model of the editor interactions on the French language Wikipedia. The results of this study will help contribute to a deeper understanding of how a framework in a different language edition of Wikipedia differs from the English. Our preliminary results show that policy plays a large role in justifying editor decisions for the edits they make on various articles. Furthermore, our research findings have expanded our knowledge of the issues surrounding replication of an English framework in a different language platform.

The English language Wikipedia is notable for its large number of articles. The development of this online encyclopedia would not be possible without the intricate interactions of editors that help sustain the virtual collaborative platform. Editors have a role in creating and reshaping articles and therefore have a role in how Wikipedia evolves over time. Our study replicates a qualitative coding scheme created over 12 years ago that demonstrates that policy and power play a role in mass collaboration. The prior work shows that user interactions including collaboration, conflict, coercion, and consensus have influenced changes on Wikipedia articles. A team of researchers are currently working to replicate this qualitative coding scheme on English Wikipedia talk pages to understand how these different types of user interactions occur in current editor discussions around article construction. Our findings will help contribute to a deeper understanding of how power plays between users have changed since the initial study. Our preliminary results, show that editor debates often lead to questions around article scope and legitimacy of sources. Furthermore, while policies play a large role in article building, editors own opinions influence how editing occurs.

Self-disclosing personal stress is an effective way of managing stress. Previous research has shown that high school students disclose more when prompted by robot that discloses vulnerability. Thus, disclosure reciprocity in human-robot interaction (HRI) provides a promising way of offering stress intervention. The current study examines how different types of disclosure from a social robot affect reciprocal human disclosure and other user outcomes through a stress-disclosing activity. Using a between-subject design, 36 young adults were randomly assigned to converse and engage in a stress-disclosing activity with a robot that shared technical facts (technical condition), feelings from other users (by-proxy condition), or feelings from itself (emotional condition). We hypothesize that emotional robot disclosure will elicit longer and deeper participant disclosure, and higher robot attributional ratings (such as likeability, perceived safety, user satisfaction, and intention for future use) than technical disclosure or by-proxy disclosure. Participant self-disclosure was measured by word count and depth of response during the robot interaction. User satisfaction, intention for future use, robot likeability, and perceived safety were captured using 5-point Likert scales. Our multivariate generalized linear model revealed that participants who experienced the technical robot disclosure condition gave the robot significantly higher rating for perceived safety, but no significant differences in other variables, such as length and depth of participant disclosure among robot disclosure conditions. Surprisingly, we found that people with higher perceived stress significantly disclosed less in both breadth and depth, and rated the robot as less likeable, had lower future intention to use and user satisfaction. This study demonstrates the complexity of designing robot for stress-intervention and the importance of capturing perceived stress as a predictor for stress intervention in HRI. Future research will examine how high stress and low stress group might perceive and interact with a social robot differently in long-term repeated interactions.

About 3.1 million adolescents are diagnosed yearly with depression. Adolescent onset of depression is associated with acute or chronic difficulties in physical, mental, and psychosocial functioning. However, over 60% of adolescents with depression do not receive mental health care, and, among those who do, treatment engagement is low. Behavioral Activation (BA) is an evidence-based psychosocial intervention for individuals with depression. While BA holds promise as an effective treatment, researchers have found that adolescents may be better reached and engaged through social and mobile technologies. In addition, BA requires frequent interaction from patients over time, which can be difficult and costly to administer in-person. There is an opportunity to improve the usability of and engagement with EBPIs via online technologies. Asynchronous Remote Communities (ARC) is a promising technology-based approach for engaging adolescents that leverages technology’s reach while providing support, social interactions, and motivation. ARCs are private online groups on which researchers can deliver weekly research tasks to participants and gather information about their perceptions in a format that is lightweight, accessible, usable, and low burden. We have used ARC to both discover design requirements and to design/build a platform for administering BA, which we have tested with clinicians and adolescents. We used ARC with 10 mental health clinicians specializing in treating teens with depression to discover their needs. Specifically, we worked with mental health clinicians to better understand their needs and to identify facilitators and barriers to adapting BA to ARC. We used the Slack online platform to create an accessible, anonymous environment where we posted 20-minute long design activities each week for 10 weeks to be completed asynchronously. We were then able to recruit teenagers to understand their needs. Based on the results, we are adapting BA to ARC settings and then testing out the feasibility.

In human-robot interaction, virtual reality (VR) provides strong potential to aid in the design of social robots. In order to understand teens' perceptions of, and interactions with social robots, we utilized a human-centered design methodology to develop a collaborative VR game for teens. The design process involved interaction studies with a local high school in three phases: (1) Brainstorming and Sketching environments on Paper, (2) Card sort game play for exploring robot design options (3) Developing and usability testing the low-fidelity VR game play. The design goals were to create an inviting game context for teens to engage in collaboration and to elicit and capture the details of teen-teen and teen-robot interactions within VR. From the design process, we found that (1) the human-centered design process engaged teens in the design process (2) teens expressed positive emotions during collaboration and (3) the asymmetric design of the VR game allowed for not only strong engagement, but also unexpected role play as teens identified themselves as the robot during gameplay. Through the design and development of this VR game, we recognized the effectiveness of a VR platform as not only a design tool, but also as an engaging collaborative platform for gathering interaction data from teens. Given this potenial, we anticipate several future applications for the study of teen-teen interaction through VR collaboration.