Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In this paper, we assess the viability of Amazon’s Mechanical Turk as a platform for graphical perception experiments. We replicate previous studies of spatial encoding and luminance contrast and compare our results. We also conduct new experiments on rectangular area perception (as in treemaps or cartograms) and on chart size and gridline spacing. Our results demonstrate that crowdsourced perception experiments are viable and contribute new insights for visualization design. Lastly, we report cost and performance data from our experiments and distill recommendations for the design of crowdsourced studies. ACM Classification: H5.2 [Information interfaces and presentation]:

Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution. In particular, we find support for social learning: newcomers who see their friends contributing go on to share more content themselves. For newcomers who are initially inclined to contribute, receiving feedback and having a wide audience are also predictors of increased sharing. On the other hand, singling out appears to affect only those newcomers who are not initially inclined to share. The paper concludes with design implications for motivating newcomer sharing in online communities. Author Keywords Social network sites, SNS, online communities, motivating

The success of Wikipedia as a large-scale collaborative effort has spurred researchers to examine the motivations and behaviors of Wikipedia’s participants. However, this research has tended to focus on active involvement rather than more common forms of participation such as reading. In this paper we argue that Wikipedia’s readers should not all be characterized as free-riders – individuals who knowingly choose to take advantage of others ’ effort. Furthermore, we illustrate how readers provide a valuable service to Wikipedia. Finally, we use the notion of legitimate peripheral participation to argue that reading is a gateway activity through which newcomers learn about Wikipedia. We find support for our arguments in the results of a survey of Wikipedia usage and knowledge. Implications for future research and design are discussed.

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www.palgrave-journals.com/ivs Design considerations for collaborative visual analytics

Leadership development practitioners have become increasingly interested in networks as a way to strengthen relationships among leaders in fields, communities, and organizations. This paper offers a framework for conceptualizing different types of leadership networks and uses case examples to identify outcomes typically associated with each type of network. One challenge for the field of leadership development has been how to evaluate leadership networks. Social Network Analysis (SNA) is a promising evaluation approach that uses mathematics and visualization to represent the structure of relationships between people, organizations, goals, interests, and other entities within a larger system. Core social network concepts are introduced and explained to illuminate the value of SNA as an