This project aims to map and analyze the publicly knowable social connections of various communities, allowing us to gain unprecedented insights about the social dynamics in such communities. Most analyses of this sort map online social networks, such as Twitter, Facebook, or LinkedIn. While these networks encode important aspects of our lives (e.g., our professional connections) they fail to capture many real-world relationships. Most of these relationships are, in fact, public and known to the community members. By mapping this publicly knowable graph, we get a unique view of the community that allows us to gain deeper understanding of its social dynamics. To this end, we built a web-based tool that is simple, easy to use, and allows the community to map itself. Our goal is to deploy this tool in communities of different sizes, including the Media Lab community and the Spanish town of Jun.

While there are a number of literacy technology solutions developed for individuals, the role of social--or networked--literacy learning is less explored. We believe that literacy is inherently a social activity that is best learned within a supportive community network, including peers, teachers, and parents. By designing an approach that is child-driven and machine-guided, we hope to empower human learning networks in order to establish an engaging and effective medium for literacy development while enhancing personal, creative, and expressive interactions within communities. We are planning to pilot and deploy our system initially in the Boston area, with eventual focus on low-income families where the need for literacy support is particularly acute. We aim to create a cross-age peer-tutoring program to engage students from different communities in socially collaborative, self-expressive, and playful literacy learning opportunities via mobile devices.

The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations such as real-world emergencies, can have harmful effects on individuals and society. Motivated by this, we are creating computational models of false and true information on Twitter to investigate the nature of rumors surrounding real-world events. These models take into account the content, characteristics of the people involved, and virality of information to predict veracity. The models have been trained and evaluated on several real-world events, such as the 2013 Boston Marathon bombings, the 2014 Ferguson riots, and the Ebola epidemic, with promising results. We believe our system will have immediate real-world applications for consumers of news, journalists, and emergency services, and that it can help minimize and dampen the impact of misinformation.

The Foodome addresses how to create deeper understanding and predictive intelligence about the relationships between how we talk and learn about food, and what we actually eat. Our aim is to build a food learning machine that comprehensively maps, for any given food, its form, function, production, distribution, marketing, science, policy, history, and culture (as well as the connections among all of these aspects). We are gathering and organizing a wide variety of data, including news/social content, recipes and menus, and sourcing and purchase information. We then use human-machine learning to uncover patterns within and among the heterogeneous food-related data. Long term, the Foodome is meant to help improve our understanding of, access to, and trust in food that is good for us; find new connections between food and health; and even predict impacts of local and global events on food.