GroundTruth: Supporting Image Geolocation with Crowdsourcing and Diagramming
This project investigates how crowdsourcing can be used to identify or verify the specific geographic location where photos were taken. For example, a journalist may need to verify photos posted on social media for a new story, an intelligence analyst may need to track down the location of a terrorist training camp, or a museum archivist may need to identify historical photos in their collections. We are studying how novices and experts perform image geolocation tasks in order to understand the mental models they create, the image clues they use, and the challenges they face. We are also developing new software tools that combine crowdsourcing with computer vision to support faster, more accurate image geolocation in a variety of domains.
Connect the Dots: Supporting Intelligence Analysis with Crowdsourcing and Visualization
This project explores how crowdsourcing can be used to help an intelligence analyst find connections within a large body of text-based evidence. For example, an analyst may have access to dozens of evidence documents, and needs to identify a hidden terrorist plot that links the evidence together. We have developed the concept of “context slices,” in which we intelligently divide up large amounts of text so that transient, novice crowd workers can contribute to solving the bigger mystery. From these ideas, we have developed Connect the Dots, a system that uses crowd workers and natural language processing techniques to build an interactive visualization from textual evidence.
Incite: Supporting Historical Research and Education with Crowdsourcing
This interdisciplinary collaboration between Computer Science, History, and Education investigates how a crowdsourcing system can support historical scholarship while helping crowd workers learn about history. We created Incite, a system that combines crowdsourcing with natural language processing techniques to help expert scholars find relevant primary sources in an digital archive. Incite was developed as a plug-in for the open-source Omeka content management system used by many museums and galleries. We have deployed Incite in high school and college classrooms across the US as part of a digital humanities project, Mapping the Fourth of July in the Civil War Era.
Civil War Photo Sleuth: Supporting Historical Research with Crowdsourcing and Computer Vision
This project, a collaboration between Computer Science, History, and Military Images magazine, seeks to recover the lost identities of portraits of the American Civil War generation. The popularity of photography exploded in the United States during the 1860s as Americans went off to war. Today, 150 years later, thousands of these photos have survived, but relatively few are identified to particular individuals. We are developing new techniques combining crowdsourcing and computer vision, including face recognition technology, to piece together visual clues from photographs and research from historical sources to solve these mysteries.
Personalized Paths: Supporting Emergency Evacuations with Crowdsourcing and Mobile Computing
This project explores how crowdsourcing and mobile technology can be combined to help people evacuate buildings more efficiently during emergency situations, such as a natural disaster or an active shooter. Emergency evacuations are challenging because large groups of people are directed to similar paths while under stress, often while avoiding obstacles or dangers. We have developed a custom smartwatch app that provides turn-by-turn indoor navigation and are running experiments to assess its feasibility in diverse emergency situations. We are also investigating how mobile devices can allow users to share information in ways that help them exit safely without advantaging human adversaries.
GraphCrowd: Improving Biological Graph Visualizations with Crowdsourcing
This project investigates how crowdsourced design can be used to improve visualizations of biological graph data. Many types of life sciences research generate graph data, but meaningful visualizations are hard to achieve with automatic graph layout algorithms, and time-consuming for experts to create manually. We have developed GraphCrowd, a system that allows novice crowd workers to design and review biological network layouts as effectively as experts. GraphCrowd is an extension of GraphSpace, a popular publication site for biological graph data. We are also collaborating with the world’s largest citizen science portal, Zooniverse, to attract participation from volunteer crowds.