NEWSFLOWS Modeling News Flows: How Feedback Loops Influence Citizens’ Beliefs and Shape Societies

Our beliefs about society are largely based on information that we encounter through media. This happens more and more in an “unbundled” form: Single news items are distributed through sharing on social media, sorted by algorithms, and encountered on platforms on which they were not originally published. Many argue that this leads to so-called “echo chambers” and “filter bubbles”, communities of people that are only exposed to information they agree with. This is thought to lead to increasing polarization of society, and to a lack of diversity in people’s (virtual) communities. But a growing body of evidence suggests that these metaphors are misleading.

In fact, as recent discussions on so-called “fake news” illustrate, biased and/or extreme information is not locked up in filter bubbles or echo chambers, but spreads from niche communities into mainstream media and politics. NEWSFLOWS develops an alternative model of how information spreads in today’s media ecosystem – a model based on so-called feedback loops, which are essential for the modern complex system of information flows. To give an example of a feedback loop: If a news item receives many shares on social media, this may let a recommendation algorithm show it to even more users (and journalists and politicans), making it more likely that they will act on it, again increasing the number of shares, etc. Crucially, neither the algorithm, nor the users, nor the writers alone determine the eventual spread, but a combination of their influences and feedback loops.

Theoretical models and empirical methods to study such feedback loops in the social sciences and humanities are scarce. NEWSFLOWS extends innovative methods as online field experiments, data donations, and automated content analysis to conduct such studies. This will greatly enhance the theoretical understanding of news flows, but also enable media organizations to develop products conforming to calls for “responsible AI”.

This project is funded by an ERC Starting Grant awarded to Damian Trilling.

Team:

  • Damian Trilling (UvA)
  • T.B.D.