This ACM MM panel aims to redefine the state of research between Academia and Industry.

This work explores the intersection between infographics and games by examining how to embed meaningful visual analytic interactions into game mechanics that in turn impact user behavior around a data-driven graphic. In contrast to other methods of narrative visualization, games provide an alternate method for structuring a story, not bound by a linear arrangement but still providing structure via rules, goals, and mechanics of play. We designed two different versions of a game-y infographic, Salubrious Nation, and compared them to a non-game-y version in an online experiment. We assessed the relative merits of the game-y approach of presentation in terms of exploration of the visualization, insights and learning, and enjoyment of the experience. Based on our results, we discuss some of the benefits and drawbacks of our designs. More generally, we identify challenges and opportunities for further exploration of this new design space.

We investigate the breaking of ties between individuals in the online social network of Twitter, a hugely popular social media service. Building on sociology concepts such as strength of ties, embeddedness, and status, we explore how network structure alone influences tie breaks - the common phenomena of an individual ceasing to "follow" another in Twitter's directed social network. We examine these relationships using a dataset of 245,586 Twitter "follow" edges, and the persistence of these edges after nine months. We show that structural properties of individuals and dyads at Time 1 have a significant effect on the existence of edges at Time 2, and connect these findings to the social theories that motivated the study.

The relationship between social sharing of emotions, social networks and social ties is an ongoing topic of research. Such sharing of emotions occurs frequently in "social awareness streams" platforms like Twitter and Facebook. We use Twitter to address research questions about the association of properties of a user's network, such as size and density, with expression of emotion in the user's Twitter posts. Our analysis suggests that expression of emotion can explain some of the variance in users' Twitter networks, and that the use of emotion in interactions between users is a strong explaining factor.