Our paper on “Building Community Resiliency through Immersive Communal Extended Reality (CXR)” was recently accepted to MDPI Multimodal Technologies and Interaction 2023.
Authors: Sharon Yavo-Ayalon, Swapna Joshi, Yuzhen (Adam) Zhang, Ruixiang (Albert) Han, Narges Mahyar, Wendy Ju.
You can find the paper here.
“Exploring how Data Scientists Review Scholarly Literature” was accepted to CHIIR 2023
Authors: Sheshera Mysore, Mahmood Jasim, Haoru Song, Sarah Akbar, Andre Kenneth Chase Randall and Narges Mahyar
You can find the paper here.
“Investigating Perceptual Biases in Icon Arrays” was accepted to CHI 2022.
Authors: Cindy Xiong, Ali Sarvghad, Cagatay Demirlap, Jake M. Hofman, Deniel G. Goldstien
You can find the paper here.
“CommunityBots: Creating and Evaluating A Multi-Agent Chatbot Platform for Public Input Elicitation” was accepted to CSCW 2023
Authors: Zhiqiu Jiang, Mashrur Rashik, Kunjal Panchal, Mahmood Jasim, Ali Sarvghad, Pari Riahi, Erica DeWitt, Fey Thurber, Narges Mahyar.
System Overview
This figure presents CommunityBots’ system overview. We use an example to guide through the process of user interactions with multiple chatbots: 1) The Household chatbot asks the user questions about their family life; 2) The user responds that they want to skip the current topic; 3) The Household chatbot receives user’s response “Skip this topic”; 4) The Household chatbot forwards the user’s response to Juji’s NLU module; 5a) Juji uses NLU to identify the user’s engagement level; 5b) NLU determines that the user doesn’t want to talk about the current topic and passes this conclusion to CommunityBot’s Topic-Switch Mechanism; 6) Topic-Switch Mechanism determines which topic to change the conversation to; 7) Since there are no remaining topics for the Household chatbot to converse, the Topic-Switch Mechanism asks the Chatbot-switch mechanism to switch from the Household chatbot to the next chatbot in queue; 8) Chatbot-switch mechanism determines that the next chatbot to converse with the user is the Work chatbot; 9) Juji notifies the Chatbot-switch mechanism about the Work chatbot invocations; 10) The Chatbot-switch mechanism fetches the questions related to the Work chatbot; 11a) The Chatbot-switch mechanism “wakes up” the Work chatbot on user’s screen and passes the next question to be asked; 11b) At the same time, the Chatbot-switch Mechanism puts the Household chatbot in a inactive state; 12) The question asked by the Work chatbot is displayed on user’s screen; 13) The user proceeds to talk to the new chatbot.
You can find the paper here.
“Mapping Instability: The Effects of the Pandemic on the Civic Life of a Small Town” was accepted to the Environments By Design: Health, Wellbeing And Place Conference 2022
Authors: Erica Dewitt, Zhiqiu Jiang, Mashrur Rashik, Kunjal Panchal, Mahmood Jasim, Fey Thurber, Cami Quinteros, Ali Sarvghad, Narges Mahyar, and Pari Riahi
You can find the paper here.
“From Invisible to Visible: Impacts of Metadata in Communicative Data Visualization” was accepted to TVCG 2022
Authors: Alyxander Burns, Christiana Lee, Thai On, Cindy Xiong, Evan Peck, Narges Mahyar
You can find the paper here.
Led by: Mahmood Jasim, Christopher Collins, Ali Sarvghad, Narges Mahyar
Related Papers:
Supporting Serendipitous Discovery and Balanced Analysis of Unstructured Text with Interaction-Driven Metrics and Bias-Mitigating Suggestions
CHI Talk
In this study, we investigate how supporting serendipitous discovery and analysis of short free-form texts, such as product review can encourage readers to explore texts more comprehensively prior to decision-making. We propose two interventions — Exploration Metrics that help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that maximizes knowledge discovery by suggesting readers sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enable readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influence readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration
If you are interested in working on or learning more about this project, please contact Mahmood Jasim at mjasim@cs.umass.edu
Led by: Brandon Reynante, Steven P. Dow, and Narges Mahyar
Related Papers: “A Framework for Open Civic Design: Integrating Public Participation, Crowdsourcing, and Design Thinking”
Civic problems are often too complex to solve through traditional top-down strategies. Various governments and civic initiatives have explored more community-driven strategies where citizens get involved with defining problems and innovating solutions. While certain people may feel more empowered, the public at large often does not have accessible, flexible, and meaningful ways to engage. Prior theoretical frameworks for public participation typically offer a one-size-fits-all model based on face-to-face engagement and fail to recognize the barriers faced by even the most engaged citizens. In this article, we explore a vision for open civic design where we integrate theoretical frameworks from public engagement, crowdsourcing, and design thinking to consider the role technology can play in lowering barriers to large-scale participation, scaffolding problem-solving activities, and providing flexible options that cater to individuals’ skills, availability, and interests. We describe our novel theoretical framework and analyze the key goals associated with this vision: (1) to promote inclusive and sustained participation in civics; (2) to facilitate effective management of large-scale participation; and (3) to provide a structured process for achieving effective solutions. We present case studies of existing civic design initiatives and discuss challenges, limitations, and future work related to operationalizing, implementing, and testing this framework.
Led by: Alyx Burns
Related Papers: Making the Invisible Visible: Risks and Benefits of Disclosing Metadata in Visualization Vis4Good Talk
Accompanying a data visualization with metadata may benefit readers by facilitating content understanding, strengthening trust, and providing accountability. However, providing this kind of information may also have negative, unintended consequences, such as biasing readers’ interpretations, a loss of trust as a result of too much transparency, and the possibility of opening visualization creators with minoritized identities up to undeserved critique. To help future visualization researchers and practitioners decide what kinds of metadata to include, we discuss some of the potential benefits and risks of disclosing five kinds of metadata: metadata about the source of the underlying data; the cleaning and processing conducted; the marks, channels, and other design elements used; the people who directly created the visualization; and the people for whom the visualization was created. We conclude by proposing a few open research questions related to how to communicate metadata about visualizations.
If you are interested in working on or learning more about this project, please contact Alyx Burns at alyxanderbur@cs.umass.edu
Led by: Alyx Burns
Related Papers: Designing with Pictographs: Envision Topics without Sacrificing Understanding
Past studies have shown that when a visualization uses pictographs to encode data, they have a positive effect on memory, engagement, and assessment of risk. However, little is known about how pictographs affect one’s ability to understand a visualization, beyond memory for values and trends. We conducted two crowdsourced experiments to compare the effectiveness of using pictographs when showing part-to-whole relationships. In Experiment 1, we compared pictograph arrays to more traditional bar and pie charts. We tested participants’ ability to generate high-level insights following Bloom’s taxonomy of educational objectives via 6 free-response questions. We found that accuracy for extracting information and generating insights did not differ overall between the two versions. To explore the motivating differences between the designs, we conducted a second experiment where participants compared charts containing pictograph arrays to more traditional charts on 5 metrics and explained their reasoning. We found that some participants preferred the way that pictographs allowed them to envision the topic more easily, while others preferred traditional bar and pie charts because they seem less cluttered and faster to read. These results suggest that, at least in simple visualizations depicting part-to-whole relationships, the choice of using pictographs has little influence on sensemaking and insight extraction. When deciding whether to use pictograph arrays, designers should consider visual appeal, perceived comprehension time, ease of envisioning the topic, and clutteredness.
If you are interested in working on or learning more about this project, please contact Alyx Burns at alyxanderbur@cs.umass.edu