Social media platforms like Facebook, Twitter, and YouTube rely on people expressing themselves freely on topics such as politics, sports, and everyday life. Since the term echo chamber has become subject to public debates, many assumptions and potentially detrimental effects have been mentioned. In terms of a working definition, we already state that echo chambers are meant as spaces that only contain information and opinions that are in line with the individual users’ pre-existing viewpoints. This, however, appears not to be a phenomenon that is solely inherent to online communication, but the fundamental human tendency to surround ourselves by similar others (“Myth 1: Ever since there are social media, people are trapped inside echo chambers”). Following this line, the public debate often suggests that on social media every single user is exclusively exposed to congruent information. But is this really the case? What is the prevalence of echo chambers on social media? Or in other words: How much “echo” does a social media user really read in his or her “online chamber”?
To answer these questions, it is necessary to investigate the current state of research in this area. Let us begin by reviewing some studies that rely on social network data. In 2015, Bakshy, Messing, and Adamic investigated the exposure to diverse political views on the social network Facebook between people who report ideological affiliations in their profiles. This analysis has shown that these Facebook users are considerably more likely to read and share news stories that are congruent with their political ideological beliefs than cross-cutting content (Bakshy, Messing, and Adamic 2015). However, 20% of users‘ Facebook friends were from the opposing party, which increases the likelihood of users receiving ideologically challenging content. There are several other studies that focus on the social network Twitter to identify how online users with different political orientations behave to each other. One of these studies uses a dataset consisting of more than a billion data points and refers to big data analysis to reveal the main differences in the pattern of political homophily among Democrats and Republicans (Colleoni, Rozza, & Arvidsson, 2014). The study combined a machine learning approach and a social network analysis to identify the political tweets in a US Twitter corpus and to distinguish the political orientation (Democrat or Republican) of the tweet. Their results show that political homophily differ between Democrats and Republicans: Democrats have a significantly higher political homophily rate than Republicans. Another study with a big data analysis used a dataset comprising 3.8 million Twitter users and a dataset of nearly 150 million tweets with political and non-political issues (Barbera, Jost, Nagler et al, 2015). The researchers demonstrated that the dissemination of information on political issues from ideologically similar sources is reproduced and distributed more frequently than information from less ideologically similar sources. Moreover, the results show that Democrats engaged significantly more frequently in cross-ideological dissemination of political and non-political information than republicans. Similarly, a recent study that used social network data from more than 250,000 U.S. Twitter users, found that people are indeed connected to politically like-minded users to a larger extent than to politically opposing users, thus providing evidence for virtual echo chambers (Boutyline and Willey, 2017). Especially those with relatively strong convictions had networks that might be referred to as echo chambers. Besides, data revealed that network homogeneity varied with ideological affiliation – Republicans’ Twitter networks were more politically homogeneous than Democrats’ networks.
While the previously reported studies rely on social network data (mostly on U.S. social media users), one could also ask users themselves how much (political) agreement versus disagreement they perceive online compared with offline. Recently, Vaccari presented in his blog results from an online survey conducted in France, Germany, and the United Kingdom after each general election in 2017, with 1,750 respondents per country (Vaccari, 2018). Here, respondents were asked, “How often do you agree with the political opinions and messages published on social media platforms by the people you follow?” and “How often do you disagree with the political opinions and messages published on social media platforms by the people you follow?”. The results indicate that social media users are more likely to disagree than agree with the political content, also showing that people encounter more disagreement in online than in face-to-face communication. This finding is in line with other studies, such as one by the Pew Research Center, which found that most Facebook and Twitter users on online networks contain a mix of people with a variety of political beliefs (Duggan & Smith, 2016).
The studies reported above lead to the preliminary conclusion that users are more frequently connected to those who have a similar ideological background. This, however, does not automatically mean that every user is exclusively exposed to information that is in line with their view: First, network analyses suggest that, to a certain extent, people are also connected and interact with dissimilar others. Second, when it comes to political discourses, people seem to perceive more disagreement than agreement on social media (and even more disagreement than in face-to-face communication). Given this pattern, we cannot point to any “echo chambers” when referring collectively to the entire group of social media users. It will be key for future research to identify the personal (e.g. pre-existing political extremity) and algorithmic circumstances under which users are inclined to become part of real echo chambers.
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132.
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber?. Psychological Science, 26(10), 1531-1542.
Boutyline, A., & Willer, R. (2017). The social structure of political echo chambers: Variation in ideological homophily in online networks. Political Psychology, 38(3), 551-569.
Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317-332.
Duggan, M., & Smith, A. (2016). The political environment on social media. Pew Research Center.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415-444.
Vaccari, C. (2018, February 2). How prevalent are filter bubbles and echo chambers on social media? Not as much as conventional wisdom has it. [Blog post]. Retrieved from https://cristianvaccari.com/2018/02/13/how-prevalent-are-filter-bubbles-and-echo-chambers-on-social-media-not-as-much-as-president-obama-thinks/