This research will examine two related issues: (1) how to motivate people to contribute more to communities such as open source software and Wikipedia that produce public goods, and (2) how to strengthen people's self-concepts and relationships with others by using content they create online to support reminiscence. This work deeply intertwines computing and social science, using insight about people's motivation, goals, and behavior to drive models, algorithms, and interfaces that leverage people's online activity to create value for individuals and society. The online nature of this activity allows it to be aggregated into large data sets for modeling (e.g., social network analysis) and mining (e.g., collaborative filtering); a major theme of the research is to effectively wring more value out of the activities people already do.
Understanding why people act online will lead to process models that explain important features of the data people generate through their actions as well as new algorithms for exploiting that data. For example, the research will model how critical events and roles people adopt affect people's contributions over time in Wikipedia. Such models models will drive algorithms that expose people to other people, groups, tools, policies, and group norms in contexts the models suggest will increase people's motivation to contribute.
Understanding users' goals will also lead to new applications for data and more effective interfaces for presenting it. The research will study how and why people reminisce through a series of lightweight prototypes that cue memories, as well as through analysis of online behavior in social media. This work will lead to algorithms that capture memory-laden content from activity in social media and interfaces that effectively use that content to support reminiscence. Preliminary work suggests that spontaneous, mobile delivery of appropriately chosen reminders promises to increase the value people derive from the content they create.
More broadly, the process of designing these models, algorithms, and interfaces will lead to insights about using social science theory in design that can be captured and shared with practitioners, new methodologies for analyzing complex social data, and the production of useful behavioral datasets that will benefit other researchers. Increasing participation in public goods like Wikipedia will improve the individual experience of members and the social goods they create. Tools developed in the domain of reminiscing have the potential to improve many people's lives, especially as the population ages. The education plan provides for richer research experiences through conference attendance and summer exchange programs with other labs. It also helps students develop the interdisciplinary attitudes and skills needed for this work through courses that look at real systems and the data they provide from both technological and social perspectives.
