This chapter looks at if and how the consumption and sharing of (mis)information are shaped by the environments that we use to communicate. Most people have heard of terms such as “echo chambers” and “filter bubbles,” but what are they exactly? And how prevalent and problematic are they in our internet-addled times? The question of whether echo chambers and filter bubbles have contributed to the spread and prevalence of misinformation is not at all obvious. This debate is ongoing, and where you land on this spectrum may affect what solutions you believe are required. This chapter offers a balanced perspective, discussing the arguments for and against the notion that echo chambers have serious implications for the spread of misinformation, polarization, and democracy.

In this chapter, we address some distinctively epistemic problems that algorithms pose in the context of social media and argue that in some cases that epistemic problems warrant paternalistic interventions. Our paternalistic proposal to these problems is compatible with respect for freedom and autonomy; in fact, we argue that freedom and autonomy demand some kinds of paternalistic interventions. The chapter proceeds as follows. First, we discuss an intervention that Facebook has run in hopes of demoting the spread of fake news on the site. We explain why the intervention is paternalistic and then, using the framework of this book, defend the intervention. We argue that while Facebook’s intervention is defensible, it is limited. It is an intervention that may pop some epistemic bubbles but will likely be powerless against echo chambers. We then discuss heavier-handed interventions that might be effective enough to dismantle some echo chambers, and we argue that at least some heavier-handed epistemically paternalistic interventions are permissible.

The aim of this chapter is to offer an exhaustive review of the literature exploring the link between social media and political polarization. It highlights the areas where a consensus based on empirical evidence has already emerged but also identifies the questions that remain open and the type of data and analysis that would help address them.

The rise of “filter bubbles,” which narrow the scope of users’ information environments, is one of the most concerning unanticipated consequences of the Internet. The question, however, is how these bubbles of polarization arise. Are they inherent in algorithmic filtering mechanisms such as recommendation engines, or do they arise from other causes as well? All algorithmic systems can be understood in terms of three elements: data, algorithmic logic, and human interactions. Abstracting in this way avoids getting caught up in the complexity and variety of data science techniques. It also counterbalances the natural tendency to focus solely on algorithms themselves. While services such as Facebook contribute to filter bubbles by algorithmically recommending content that reinforces existing viewpoints, what users share to begin with and what they click on once surfaced by the algorithm also matter a great deal. A simulation experiment demonstrates how filter bubbles emerge or collapse from the interactions of all three factors.