Research Lab
News Bridge studied how the language that a media outlet – PBS’ Frontline – uses to promote its content influences the political diversity of its audience. In this project we tracked user engagement with tweets posted by Frontline over three years and built models that, given the tweet text, predict the political diversity of the audience. We then integrated the models into a web app that helped Frontline craft tweets engaging to a politically diverse audience, guided by the model predictions. While studies of political polarization on social media typically investigate the behaviors of individual users, New Bridge focused on the media outlets’ impact on audience fragmentation and developed tools that can help them reduce it. We believe this approach can be further developed and generalized into tools to help communicators (e.g., in public health) engage audiences across political, social, and/or cultural boundaries.
Exploring new rituals, formats, and structures for coming together
Pilots & Programs
An auditable AI framework for tracing competing narratives across podcasts, conversations, and news
Research
Discovering semantically or emotionally salient moments in spoken discourse using LLMs
Research
A new civic infrastructure in Boston grounded in dialogue as a way to building “civic muscle” of democracy
Pilots & Programs
An AI interface that turns raw conversation audio into interactive maps
Research
A training ground to practice consensus-finding with real human perspectives
Research