We introduce STRIVE, a framework to build socio-behavioral taxonomies in multiplayer online games using unsupervised learning on common features across many social games.This work demonstrates this framework on "Sky: Children of the Light," a social adventure game by Thatgamecompany, using features such as cooperative play, social bonding, and in-game communication.After performing descriptive statistics, clustering and dimensionality reduction, we assign semantic categories to these behavior clusters.Next we perform a behavior prediction experiment where the most social cluster's behavior has a higher correlation with future play time and chats sent than predicting on the full dataset.These results suggest the importance of customized, or personalized, player behavior prediction models.Moreover, this framework could be easily extended to other games and further augment our understanding of human behavior in virtual worlds, ultimately aiding social scientists and game designers to better match players together for healthier online interactions.

People and organizations are increasingly using online platforms to assemble teams. In response, HCI researchers have theorized frameworks and created systems to support team assembly. However, little is known about how users search for and choose teammates on these platforms. We conducted a field study where 530 participants used a team formation system to assemble project teams. We describe how users' traits and social networks influence their teammate searches, teammate choices, and team composition. Our results show that (a) what users initially search for differs from what they finally choose: initially they search for experts and sociable users, but they are ultimately more likely to choose their prior social connections as their teammates; (b) users' decisions lead to non-diverse and segregated teams, where most of the expertise and social capital are concentrated in a few teams. We discuss the implications of these results for designing team formation systems than promote users' agency.

Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors' use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise pre-existing knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.

With the ubiquity of wearable computing, an important and emerging challenge is to understand how to design wearable information displays for non-visual, non-auditory interaction. This is particularly relevant to the design of accessible technologies for people with vision impairments. Working towards this aim, we developed a smartwatch prototype that uses variable friction surface haptics to test initial design concepts. Through interviews and iterative prototyping with seven blind users, we identified three key use cases for a haptic smartwatch as well as embodied conceptual models for presenting haptic information. We found that a physical clock face, compass, and numerical keypad are productive representations for presenting information haptically, and these models build on existing tactile and spatial understandings of our target user group.