Current approaches to physical crowdsourcing focuses on one person contributing full reports for participatory sensing. These approaches focus on opportunistic sensing, either through listening passively on someone’s phone, or catching particular moments in which people interact with their phone, like when someone unlocks their phone with Twitch. This doesn’t allow us to get high-fidelity data and ask targeted questions about the events we want to collect data on. Existing crowdsourcing efforts are relatively self-contained and simply aggregate these contributions. We care about the ability to decompose a single location-based data collection task (that might otherwise require a lot of effort from a single person) to many mobile users, and then scaffold those smaller, lightweight individual contributions to reach the same (if not higher) fidelity of existing crowdsourcing methods given the constraints of an on-the-go crowd.

We present BiteNow, a mobile application that supports people tracking free food on campus while on-the-go using the interaction techniques of TapShare and Gaze. We believe that by both contributing and receiving useful information, users will be more likely to use BiteNow in the long term. In addition, we are integrating features from Gaze to add details about free food reports that would be impossible to convey only using TapShare's double-tap interaction, such as how much food is left and the floor/room number it is located at in a building.

BiteNow's main contribution is the idea of opportunistically enabling low-effort contributions to citizen science or participatory communitysensing by connecting people to short data contribution opportunities while on-the-go. The ease and speed of reporting enabled a new form of lightweight contribution that has the potential to broaden participation to a larger, mobile crowd. However, one main challenge is that the on-the-go crowd might not be willing to take time out of their day to contribute; the effort can be too high. There is also a trade-off between the cost of contribution and the fidelity of data collected. In order to address these challenges, we used low-effort reporting techniques (TapShare's double tap gesture) and the Gaze design pattern (Identify-Focus-Capture using iOS8 interactive notifications) to make contribution as low-effort as possible. Since lowering the effort to contributing to crowdsourcing efforts can lead to lower fidelity data, we implemented a scaffolding mechanism to aggregate and combine low-effort contributions into a high fidelity report.

We conducted two field deployments of BiteNow . The first was a usability test and the second divided participants into a control and experimental group to test our hypothesis that this interaction/system will encourage the number of overall contributions to on-the-go mobile crowdsourcing efforts. Our two user studies demonstrated that low-effort tasks could be chained together to progressively gather information about physical phenomena. From our post-survey results, we found that tapping was low-effort but somewhat problematic, as some users thought the interaction was “too easy,” often leading to false positive in the form of accidental initial reports. On the other hand, verification tasks were easy to answer, intuitive, and accurate. Overall, the end-to-end interaction using TapShare and Gaze was considered easier than the form-based report of the control app and led to more contributions in the form of initial reports and verification tasks.

BiteNow image 1

Figure 1: BiteNow finds out where free food is on campus by asking many users simple questions and notifies the interested ones about it when they come, close to food

BiteNow image 2

Figure 2: The flow of a food report, from creation to populating the newsfeed of free food nearby.



  • None

Ph.D. Students

  • None

Masters and Undergraduate Students

  • 🎓 Nicole Zhu
  • 🎓 Stephen Chan
  • 🎓 Zak Allen