Measuring Online Social Bubbles

By Dimitar Nikolov, Diego F. M. Oliveira, Alessandro Flammini, Filippo Menczer. 2015

200 citations recorded by the source corpus.

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.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Since users tend to consume more content from like-minded people, such consumption biases were linked to the creation of ﬁlter bubbles (Nikolov et al., 2015).”
Quantifying Biases in Online Information Exposure (2018, methodology): “org) using a procedure described in the literature (Nikolov et al., 2015).”
The spread of low-credibility content by social bots (2017, unknown): “These include information overload and finite attention [41], novelty of false news [57], the selective exposure [52, 40, 39] caused by polarized and segregated online social networks [9, 8], algorithmic popularity bias [45, 20, 37], and other cognitive vulnerabilities such as confirmation bias and ”
Hoaxy: A Platform for Tracking Online Misinformation (2016, background): “…underlying social media create peculiar patterns of information access. homophily [25], polarization [12], algorithmic ranking [3], and social bubbles [28] — information environments with these challenges, here we present Hoaxy , an upcoming platform for the tracking of social news sharing.”
Diversity in Big Data: A Review (2017, unknown): “21,32 topical diversity of content and of users was determined based on network structure and on information directly reported by the users, and not on content analysis.”
False Information on Web and Social Media: A Survey (2018, background): “[67] by demonstrating that the diversity of sources (links) clicked by users is significantly lower on social media platforms than in general search engines.”
Limited individual attention and online virality of low-quality information (2017, background): “This may further lead to polarization [57, 13, 56, 43]; one group may automatically discount ideas from another [39, 44].”