Research
Penn Media Accountability Project (PennMAP)
PennMAP is building technology to detect patterns of bias and misinformation in media from across the political spectrum and spanning television, ratio, social media, and the broader web. We will also track consumption of information via television, desktop computers, and mobile devices, as well as its effects on individual and collective beliefs and understanding. In collaboration with our data partners, we are also building a scaleable data infrastructure to ingest, process, and analyze tens of terabytes of television, radio, and web content, as well as representative panels of roughly 100,000 media consumers over several years.
COVID – Philadelphia
Our team is building a collection of interactive data dashboards that visually summarize human mobility patterns over time and space for a collection of cities, starting with Philadelphia, along with highlighting potentially relevant demographic correlates. We are estimating a series of statistical models to identify correlations between demographic and human mobility data (e.g. does age, race, gender, income level predict social distancing metrics?) and are using mobility and demographic data to train epidemiological models designed to predict the impact of policies around reopening and vaccination.
High-Throughput Experiments on Group Dynamics
To achieve replicable, generalizable, scalable, and ultimately useful social science, we believe that is necessary to rethink the fundamental “one at a time” paradigm of experimental social and behavioral science. In its place we intend to design and run “high-throughput” experiments that are radically different in scale and scope from the traditional model. This approach opens the door to new experimental insights, as well as new approaches to theory building.
Common Sense
This project tackles the definitional conundrum of common sense head-on via a massive online survey experiment. Participants are asked to rate thousands of statements, spanning a wide range of knowledge domains, in terms of both their own agreement with the statement and their belief about the agreement of others. Our team has developed novel methods to extract statements from several diverse sources, including appearances in mass media, non-fiction books, and political campaign emails, as well as statements elicited from human respondents and generated by AI systems. We have also developed new taxonomies to classify statements by domain and type.
News
Can Social Media Be Less Toxic?
In an era where online interactions can be both motivational and toxic, researchers from the Computational Social Science Lab (CSSLab) at the University of Pennsylvania are interested in what encourages prosocial behavior — acts of kindness, support, and cooperation — on social media.
News On Climate Change Is More Persuasive Than Expected, Study Finds
Climate change is one of the most pressing challenges of our time, demanding urgent and effective action to mitigate its severe impacts. One barrier to effective climate change action is its polarizing nature largely driven by the media, as people prefer to consume news that aligns with their political beliefs. This tendency is especially strong among climate skeptics, who are more inclined to seek information that reinforces their views on climate change. In this context, communication—especially on social media—plays a crucial role in bridging cross-partisan boundaries. However, meaningful dialogue may be hampered if individuals do not believe these interactions will be effective.
From Cracks to Gardens: Creating a Thriving Social Media Through Research
Early advocates of social media believed that the creation of these platforms would lead to positive outcomes. When Facebook was launched in 2004, it was praised for its ability to “connect the entire world.” In hindsight, many of these ideals were optimistic at their time as social media platforms are often criticized for spreading hate and misinformation.