Measuring online social bubbles

Abstract

Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed-Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside "social bubbles." Our results could lead to a deeper understanding of how technology biases our exposure to new information.

Hoaxy

“…Online social networks are characterized by homophily [25], polarization [12], algorithmic ranking [3], and social bubbles [28] -information environments with low content diversity and strong social reinforcement.…”

Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections

“…Analogously, the dynamics of the electoral campaign online media has a (relatively) long literature, focusing, time by time, on USA [8][9][10][11][12][13][14], Australia [15,16], Norway [17], Spain [18], Italy [19][20][21], France [22] and UK [23,24]. Generally, the shift from mediated to disintermediated news consumption has led to a range of documented phenomena: users tend to focus on information reinforcing their opinion (confirmation bias [25][26][27][28][29]) and to group in clusters of people with similar viewpoints, forming the so called echo chambers [26][27][28][29][30][31]. The different dynamics that the public debate follows on social-network platforms is also remarkable: the time evolution of viral non-verified contents is more persistent than the verified equivalent [27] and "negative" messages spread faster than "positive" ones, even if the latter reach on average a wider audience [32].…”

Me, My Echo Chamber, and I

“…We also evaluate the effect of treatments on the political diversity of each participant's social network 6 1, 2, and 3 weeks after use. In particular, we use Shannon's entropy similar to other studies exploring network connection diversity [5,9,22] to quantify the balance of left and right-leaning accounts the user follows. Under this metric, 6 Namely, their "followees" -i.e., who they follow on Twitter.…”