Few terms have grabbed the public attention as much as fake news over the past few years, but the nascent nature of the phenomenon mean that we’re still largely feeling our way in terms of understanding it. A new paper from the Wharton School aims to help by providing a framework for the study of media bias and misinformation.
The emergence of fake news in the wake of the Brexit referendum and the Trump election in 2016 triggered a wave of research into the phenomenon. Much of this research focused on the spreading of outright lies on social media, but the Wharton team believe that a far more pressing concern is misinformation in its broadest sense.
Misinformation covers far more that just lies and includes the various subtle ways in which people are misled and manipulated. For instance, data might be cherry picked or correlation and causation may be misinterpreted.
Misinformation is also a problem that spreads far beyond social media and also surfaces on more traditional media, such as television and print. This isn’t reflected in many of the studies into the topic, however, which tend to focus on social media.
“All of the research that has been done on Twitter vastly outweighs the amount of research that has been done on TV in the last four years, and yet TV is a larger source of information related to politics for typical Americans than Twitter is.” the researchers say. “We really have to be thinking much more expansively about the parts of the information ecosystem that might be causing some these problems.”
Plugging the gap
The researchers set out to plug this gap and aimed to do so in a number of ways. Firstly, they wanted to develop an infrastructure to allow for the collection, organization, and cleaning of data to make it readily available to the research community.
“If you want to look at everything that is being produced on television, radio, and the web and ask questions, there’s no way to answer them right now,” they explain. “There doesn’t exist any infrastructure to collect that data, and even just collecting that data is an enormous undertaking.”
Next they wanted to ensure greater coordination of efforts by the various research groups in operation. The model developed by the team aims to allow for the study of problems more holistically and avoid each study plowing their own furrough.
The researchers also accept that this work is a wide ranging endeavor so want to make it easier to communicate their findings to the public while also developing a richer ecosystem of academic-industry partnerships.
“It would already be a big step for people of different disciplines and institutions to work together on a common dataset, but, if we want to actually solve problems in the world, we have to do more than just understand things. We have to also try to design interventions that affect people’s experience on real platforms and measure the consequences,” they say.
The work will be coordinated via a new facility launched at Penn earlier this year, called the Computational Social Science Lab. It already has a number of projects under way, including examining the prevalence of radical content on YouTube and the changing media consumption as we switch from television to streaming platforms.
“Many researchers can then start using this data, and then the amount of research that is generated by this infrastructure goes up by 100 or one hundred times. I think that will be the real innovation,” he says. “There’s many questions out there, and we would like to be able to help lots of people answer those questions.”
