Preliminary Conference Agenda

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Session Overview
Session
Papers 10: Data-Driven Storytelling and Modeling
Time:
Monday, 01/Apr/2019:
3:30pm - 5:00pm

Session Chair: Matthew Andrew Willis, University of Oxford
Location: 2100/2101/2102

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Presentations

Engaging the Community Through Places: An User Study of People's Festival Stories

X. Wang, T. Knearem, H. J. Yoon, H. Jo, J. Lee, J. Seo, J. M. Carroll

Pennsylvania State University, United States of America

People’s lived experiences, stories, and memories about local places endow meaning to a community, which can play an important role in community engagement. We investigated the meaning of place through the lens of people’s memories of a local arts festival. We first designed, developed, and deployed a web application to collect people’s festival stories. We then developed our interview study based on 28 stories collected through the web app in order to generate rich conversations with 18 festival attendees. Our study identifies three parallel meanings that a place can hold based on the following types of festival attendees: experience seekers, nostalgia travelers, and familiar explorers. We further discuss how information technology can facilitate community engagement based on those parallel meanings of place.



Understanding Partitioning and Sequence in Data-Driven Storytelling

Z. Zhao1, N. Elmqvist1, R. Marr1, J. Shaffer2

1University of Maryland, College Park, United States of America; 2United States Naval Academy

The comic strip narrative style is an effective method for data-driven storytelling. However, surely it is not enough to just add some speech bubbles and clip art to your PowerPoint slideshow to turn it into a data comic? In this paper, we investigate aspects of partitioning and sequence as fundamental mechanisms for comic strip narration: chunking complex visuals into manageable pieces, and organizing them into a meaningful order, respectively. We do this by presenting results from a qualitative study designed to elicit differences in participant behavior when solving questions using a complex infographic compared to when the same visuals are organized into a data comic.



Modeling adoption behavior for innovation diffusion

E. Zhou1, D. Li1, A. Madden1, Y. Chen1, Y. Ding2, Q. Kang1, H. Su1

1School of Information Management, Sun Yat-Sen University, Guangzhou, China; 2School of Informatics and Computing, Indiana University, Bloomington,United States

Studying diffusion of innovation is getting critical given in the current AI era, an increasing number of new technologies have been developed to promote disruptive innovation. Unlike previous works which mainly consider direct influence between new technology adoption behaviors, a new model named as Adoption Behavior based Graphical Model(ABGM) is proposed by incorporating influence factor (i.e., homophily and heterophily) among users' adoption behavior towards new AI technologies. This model simulates the process of innovation diffusion and learns the diffusion patterns in a uni ed framework. We evaluate

the proposed model on a large-scale AI publication dataset from 2006 to 2015. Results show that ABGM outperforms start-of-art baselines and also demonstrates that the probability of individual user adopting an innovation is significantly influenced by the diffusion process through the correlation network.



 
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