Under-contribution is a problem for many online communities. Social psychology theories of social loafing and goal-setting can provide mid-level design principles to address this problem. We tested the design principles in two field experiments. In one, members of an online movie recommender community were reminded of the uniqueness of their contributions and the benefits that follow from them. In the second, they were given a range of individual or group goals for contribution. As predicted by theory, individuals contributed when they were reminded of their uniqueness and when they were given specific and challenging goals, but other predictions were not borne out. The paper ends with suggestions and challenges for mining social science theories as well as implications for design.

With new technology, people can share information about everyday places they go; the resulting data helps others find and evaluate places. Recent applications like Dodgeball and Sharescape repurpose everyday place information: users create local place data for personal use, and the systems display it for public use. We explore both the opportunities --new local knowledge, and concerns --privacy risks, raised by this implicit information sharing. We conduct two empirical studies: subjects create place data when using PlaceMail, a location-based reminder system, and elect whether to share it on Sharescape, a community map-building system. We contribute by: (1) showing location-based reminders yield new local knowledge about a variety of places, (2) identifying heuristics people use when deciding what place-related information to share (and their prevalence), (3) detailing how these decision heuristics can inform local knowledge sharing system design, and (4) identifying new uses of shared place information, notably opportunistic errand planning.

The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, "home", "work" or "Northwest Health Club". It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices.Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than "points" are required to represent the full range of people's everyday places.

Personal gazetteers record individuals' most important places, such as home, work, grocery store, etc. Using personal gazetteers in location-aware applications offers additional functionality and improves the user experience. However, systems then need some way to acquire them. This paper explores the use of novel semi-automatic techniques to discover gazetteers from users' travel patterns (time-stamped location data). There has been previous work on this problem, e.g., using ad hoc algorithms [13] or K-Means clustering [4]; however, both approaches have shortcomings. This paper explores a deterministic, densitybased clustering algorithm that also uses temporal techniques to reduce the number of uninteresting places that are discovered. We introduce a general framework for evaluating personal gazetteer discovery algorithms and use it to demonstrate the advantages of our algorithm over previous approaches.

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