When social networking sites give users granular control over their privacy settings, the result is that some content across the site is public and some is not. How might this content--or characteristics of users who post publicly versus to a limited audience--be different? If these differences exist, research studies of public content could potentially be introducing systematic bias. Via Mechanical Turk, we asked 1,815 Facebook users to share recent posts. Using qualitative coding and quantitative measures, we characterize and categorize the nature of the content. Using machine learning techniques, we analyze patterns of choices for privacy settings. Contrary to expectations, we find that content type is not a significant predictor of privacy setting; however, some demographics such as gender and age are predictive. Additionally, with consent of participants, we provide a dataset of nearly 9,000 public and non-public Facebook posts.

Recent news has made social media notorious for both abusing user data and allowing governments to scrutinize personal information. Nevertheless, people still enjoy connecting with friends and families through social media but fail to use it to connect to local communities where we live our daily lives. In this paper, we present Popup Networks, a new platform for building hyper-local social computing applications, running on home wireless routers via an underlying mesh network. Summative interviews illustrate interests in using Popup Networks to create new local ties and as a backup in the case of Internet disruption. By utilizing locality to ward off external risks, Popup Networks provide alternative privacy, visibility, and economic models compared to traditional social media. While deploying Popup Networks would be an ideal evaluation, we argue that the technical tests and user interviews we conducted are suitable for socially complex systems such as Popup Networks--advocating an agenda moving forward for social computing systems research.

Crowd-sourcing social computing systems represent a new material for HCI designers. However, these systems are difficult to work with and to prototype, because they require a critical mass of participants to investigate social behavior. Service design is an emerging research area that focuses on how customers co-produce the services that they use, and thus it appears to be a great domain to apply this new material. To investigate this relationship, we developed Tiramisu, a transit information system where commuters share GPS traces and submit problem reports. Tiramisu processes incoming traces and generates real-time arrival time predictions for buses. We conducted a field trial with 28 participants. In this paper we report on the results and reflect on the use of field trials to evaluate crowd-sourcing prototypes and on how crowd sourcing can generate co-production between citizens and public services.