The Electome: Fueling the Horse Race of Ideas in the 2016 Election
Soroush Vosoughi, Prashanth Vijayaraghavan, Margaret Hughes, William Powers, Andrew Heyward, Russell Stevens and Deb Roy
Project Alumni: Sophie Chou, Perng-Hwa Kung, Eric Chu, Neo (Mostafa) Mohsenvand, Raphael Schaad
The “Electome” is a comprehensive mapping of the content and network connections among the campaign’s three core public sphere voices: the Candidates, the Media, and the Public. This mapping is used to trace the election’s narratives as they form, spread, morph and decline among these three groups – identifying who and what influences these dynamics. We are developing metrics that show how responsive the Candidates and Media are to the issues that the Public find most relevant and important.
LSM’s analytics will serve as the basis of news coverage that LSM will produce in collaboration with major media partners. The ultimate aim is to offer an alternative to the “horse race journalism” that has dominated election news for the last half-century. The Electome will instead focus on surfacing the important issues at stake in the campaign, or the “The Horse Race of Ideas.”
The John S. and James L. Knight Foundation is providing support for the Electome project as part its efforts to advance excellence in journalism and increase civic engagement. For a complete list of LSM’s “Horse Race of Ideas” published articles, please click here.
Playful Words: Social Literacy Learning
Ivan Sysoev, Anneli Hershman, Mina Soltangheis, Eric Chu, Juliana Nazarè, Nazmus Saquib, Sneha Priscilla Makini, Preeta Bansal and Deb Roy
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 an inherently 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 a 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 Foodome: Building a Comprehensive Knowledge Graph of Food
Neo (Mostafa) Mohsenvand, Prashanth Vijayaraghavan, Soroush Vosoughi, Goulong Luke Wang, Russell Stevens and Deb Roy
The “Foodome” addresses how we might create deeper understanding and predictive intelligence about the relationships between how we talk about food, how we 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).
LSM is gathering and organizing a wide variety of data – news/social content, recipes and menus, sourcing and purchase information, etc. 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 never seen before; and even predict impacts of local and global events on food.
Target is providing funding support for the Foodome as part of its Food and Future coLAB effort designed to “push the edges of science, technology and design to give people better control over their food choices and help them to eat healthier.”
Visible Communities is a system that combines what local people using smartphones see on the ground with what computers can detect from satellite images, to create an interactive map at a fine-resolution that continuously improves. The map captures both spatial and social data: houses and the paths connecting them, and the households living there and their relationships.
Enabling communities to put themselves on the map is a powerful way to increase their own visibility, and in turn serves institutional needs to improve infrastructure planning and humanitarian aid delivery. Existing approaches to do community-driven mapping either require outside experts to facilitate, or the results are lower tech and not easy to keep up to date.
In partnership with Partners in Health (PIH), and supported by the MIT Tata center, we are piloting this social machine in a sparsely populated hilly region with a Community Health Worker (CHW) network in Burera, Rwanda.
The smartphone app enables CHWs to self-map their communities. We are intentionally designing an intuitive pre-literacy touch interface enabling a wide range of users to participate without much training. By removing barriers for people at the base of the socio-economic pyramid and designing with natural social dynamics in mind, we hope to unlock existing, self-motivated human potential.
Continuously improving system via integration of top-down satellite image analytics with bottom-up ground-truth data.
AINA: Aerial Imaging and Network Analysis
Neo (Mostafa) Mohsenvand and Deb Roy
This project is aimed at building a machine learning pipeline that will discover and predict links between the visible structure of villages and cities (using satellite and aerial imaging) and their inhabiting social networks. The goal is to estimate digitally invisible villages in India and Sub-Saharan Africa. By estimating the social structure of these communities, our goal is to enable targeted intervention and optimized distribution of information, education technologies, goods, and medical aid. Currently, this pipeline is implemented using a GPU-powered Deep Learning system. It is able to detect buildings and roads and provide detailed information about the organization of the villages. The output will be used to construct probabilistic models of the underlying social network of the village. Moreover, it will provide information on the population, distribution of wealth, rate and direction of development (when longitudinal imaging data is available), and disaster profile of the village.
Martin Saveski, Soroush Vosoughi, Eric Chu, and Deb Roy
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.
Responsive Communities: Pilot Project in Jun, Spain
Martin Saveski, William Powers, and Deb Roy
To gain insights into how digital technologies can make local governments more responsive and deepen citizen engagement, we are studying the Spanish town of Jun (population 3,500). For the last four years, Jun has been using Twitter as its principal medium for citizen-government communication. We are mapping the resulting social networks and analyzing the dynamics of the Twitter interactions, in order to better understand the initiative’s impact on the town. Our long-term goal is to determine whether the system can be replicated at scale in larger communities, perhaps even major cities.
- “The Incredible Jun: A Town that Runs on Social Media,” by LSM’s William Powers and Deb Roy in Medium, 4/15/15
Rumor Gauge: Automatic Detection and Verification of Rumors on Twitter
Soroush Vosoughi and Deb Roy
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.