“You are living in an echo chamber my friend! You know not everyone thinks like you do, right?” The term “echo chamber” has recently become part of our daily lexicon, although the concept has existed for close to 20 years, with the publication of Cass Sunstein’s Republic.com 2.0, followed by #Republic, keeping up with internet trends of course. The echo chamber generally refers to a network that can be characterized as highly insular and one-sided; a place in which confirmation bias, polarization, and opinion extremism find fertile ground. To some degree or another we are all living in echo chambers, at least with regards to some topic or issue that is important to us – perhaps a sports team, a political issue, or a religious belief.
Some have suggested that a network can be characterized as an echo chamber when the information spread within it on a given topic is 95% one-sided. However, there is certainly nothing wrong with a network where 95% of the information agrees that the world is indeed round, or that poverty is bad. Beyond this, given the masses of content, and the subjectivity of assessing the bias of each piece of content and each post, how would such analysis ever be possible? Rather, the network structure characteristics, such as density, insularity/inwardness, centrality, and reciprocity, which are common metrics used by social network analysts, provide the most assessable and theoretically-based proxies by which to measure the echo chamber.
While the echo chamber generally refers to any type of social network, it is more commonly used to refer to online networks. Indeed, the echo chamber effect is stronger online than it is offline. To explain why this may be the case, we need only to turn to Eli Pariser’s now famous “Filter Bubble” hypothesis. According to Pariser, the personalization algorithms that control our online lives, dictate what information we will and will not see, including recommendations for content, purchases, and friends, act as an environmental factor, shaping and determining network structures. While there has been a tendency to use the filter bubble and echo chamber interchangeably, this is clearly incorrect. The echo chamber is a type of network, while the filter bubble is a process which impacts it.
In a recent study we randomly allocated new Twitter users to a treatment of “filter bubble suppression,” whereby users used new email addresses, declined all automated recommendations, and disabled personalization algorithms. After three months of using Twitter, participants from both the treatment and control groups completed a survey that assessed their justification and support for terrorism. The study found a significant interaction effect between the treatment and network inwardness – the primary proxy for the echo chamber – as well as network density, whereby being in the treatment group and having a more outwardly-focused network decreased the odds of justifying terrorism.
Criminologists have long regarded the role of individuals within networks as the source from which deviant beliefs and attitudes are learned. However, one small group of researchers has long suggested that network structure characteristics may be more important determinants of deviant beliefs and attitudes than the identity of the network members themselves. The findings of the Twitter experiment may serve to support this theoretical perspective and suggest that while echo chambers can be incubators of radicalization, it is also possible to burst their bubble.