All about Li (Leo) Xiong

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  • My dissertation project

    It is a longitudinal field experiment that examines whether explicit features such as reputation systems and tags influence people's perception of reciprocity and social identification, and if these perceptions have an impact on their participation in online sharing communities. It is ongoing now so I can't really say too much about the design. But it is fun and exhausting. I became a better Python programmer and web developer for this experiment, and have a great deal to share with my committee (and my students) about what I learnt from (trying) building an online community. Most importantly, I learned how to design interactions that sustain a community, and I learned what it was like to build a technology that people use.

  • The Bump sharing network project

    I'm analyzing about 9 million rows of user logs to see where, when and how people share content with an iPhone app called Bump. My analysis involves the structural signatures and attributes of networks over 5 months in 100 metropolitan areas in the US. It will reveal a great deal of information of how people use mobile apps to share content over time.

  • The virtual world behavior prediction project

    This is a large grant project that examines how people of different characteristics behave differently with their avatars in online games. I am leading a group on predicting how extraverted people behave. We are working on a paper right now. It is fun but I hope to get over it pretty soon. We just got about 16,000 responses from a survey we launched with a big Chinese game company. Now we can tell what really drives the Chinese players to play online games.

  • The online community information credibility project

    In this project, we examine how reviewers' attributes such as their gender and popularity influence people's evaluation of their opinions. We started with Yelp.com, but we hope the results are applicable to all online commmunities that are based on information sharing. We are trying to build a social-heuristic model for information credibility online.