Exposure to ideologically diverse news and opinion on Facebook

By Eytan Bakshy, Solomon Messing, Lada A. Adamic. 2015

2,992 citations recorded by the source corpus.

Subfields: Communication, Sociology and Political Science, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Ranking with Fairness Constraints (2017, background): “…stereotypes by over/under-representing sensitive attributes such as race and gender [KMM15, BCZ + 16], to news feeds that can promote extremist ideology [CHRG16] and possibly even aﬀect the results of elections [Bae16, BMA15], it is clear that ensuring fair or unbiased rankings is often crucial.”
Political Polarization on the Digital Sphere: A Cross-platform, Over-time Analysis of Interactional, Positional, and Affective Polarization on Social Media (2020, background): “…emotional user experience, Facebook has become the prime suspect for the creation of homophilic echo chambers (Ben-David & Matamoros-Fernandez, 2016; Nahon, 2016), and numerous studies have confirmed high levels of homophily among Facebook users (e.g., Bakshy et al., 2015; Bond et al., 2012).”
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign (2018, background): “Findings that focused on the positive effects of social media, such as increasing voter turnout [10] or exposure to diverse political views [7] contributed to the general praise of these platforms as a tool for promoting democracy and civic engagement [24], [38], [51], [54], [55].”
The growing ubiquity of algorithms in society: implications, impacts and innovations (2018, background): “Bastos [11], also in this issue, discusses other aspects of the human–algorithmic interface, some of which have also been described in the analysis of political polarisation [104,105].”
The Effect of Recommendations on Network Structure (2016, background): “The empirical investigations of this issue have found the eﬀects to be real, but often relatively small in magnitude [5, 12].”
The consequences of online partisan media (2021, background): “These dynamics have eroded the power of traditional gatekeepers to set the boundaries of debate, allowing a wider range of perspectives—but also rumors, misinformation, and biased accounts of reality—to ﬂourish (3– 5).”
Characterizing the 2016 Russian IRA influence campaign (2018, background): “…that focus on the positive effects of social media, such as increasing voter turnout Bond et al. (2012) or exposure to diverse political views Bakshy et al. (2015) contribute to the general praise of these platforms as a tool for promoting democracy and civic engagement Effing et al. (2011);…”
Anger, Fear, and Echo Chambers: The Emotional Basis for Online Behavior (2019, background): “To date, the empirical evidence remain mixed, suggesting that social media increase selective exposure but not to the extent envisaged by some scholars (Bakshy et al., 2015; Brundidge, 2010; Dvir-Gvirsman, Tsfati, & Menchen-Trevino, 2016).”
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online (2019, background): “…which some have likened to a “dumpster fire” (Maza, 2018) and often represent partisan bias more than thoughtful and balanced discussions of issues (Bakshy, Messing, & Adamic, 2015; Barberá et al., 2015; Brady et al., 2017; Colleoni, Rozza, & Arvidsson, 2014; Himelboim, Mccreery, & Smith, 2013).”
The echo chamber effect on social media (2021, background): “Our attention span remains limited (9, 10), and feed algorithms might limit our selection process by suggesting contents similar to the ones we are usually exposed to (11–13).”
SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale (2019, background): “” These stories are then characterized into dense feature vectors by feature extractors and learned models [17,26–28].”
Quantifying Biases in Online Information Exposure (2018, methodology): “Also on Facebook, three ﬁlters — the social network, the news feed algorithm, and a user’s own content selection — signiﬁcantly decrease exposure to ideologically challenging news (Bakshy, Messing, & Adamic, 2015).”