"I always assumed that I wasn't really that close to [her]": Reasoning about Invisible Algorithms in News Feeds

By Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, A. Vuong, K. G. Karahalios, Kevin Hamilton, Christian Sandvig. 2015

676 citations recorded by the source corpus.

Subfields: Computer Science Applications, Safety Research, Human-Computer Interaction

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Citations in the corpus

Breaking the filter bubble: democracy and design (2015, background): “Recent studies indicate that most people are unaware of filters in social media (Eslami et al. 2015; Rader and Gray 2015).”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “At one point, most users were unaware that Facebook newsfeeds were not simply all the posts that their friends made [24].”
‘Hypernudge’: Big Data as a mode of regulation by design (2017, neutral): “…opacity of the underlying algorithms and the lack of awareness or understanding by many digital service users of their significance and operation (Eslami et al., 2015) and the dominance of a handful of extraordinarily powerful transnational companies in a global networked market for digital…”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “Many Facebook users do not realize that the content in their News Feeds is curated by an algorithm [16], so they cannot react to stories in the News Feed like they would a recommendation.”
First I "like" it, then I hide it: Folk Theories of Social Feeds (2016, background): “Previous work investigated users’ awareness of curation algorithms, paths toward awareness and effects of awareness [9, 29], yet much remains unknown about which folk theories exist to make sense of these algorithms, the connection between design and the development of folk theory, and the utility o”
Algorithmic Mediation in Group Decisions: Fairness Perceptions of Algorithmically Mediated vs. Discussion-Based Social Division (2017, background): “This work adds to research on the importance of algorithmic transparency and accountability [16, 47], and suggests that more research needs to be done to understand how to design algorithms with a level of transparency that is actually understandable to and useful for people.”
Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products (2019, background): “Given these risks, much algorithmic audit work has focused on goals to gauge user awareness of algorithmic bias [17, 26] or evaluate the impact of bias on user behaviour and outcomes [16, 19, 24], instead of directly challenging companies to change commercial systems.”
Communication, Mediation, and the Expectations of Data: Data Valences Across Health and Wellness Communities (2015, background): “For example, many people have a different awareness of and expectation for the algorithm that parses their Facebook feed than the reality of the engineering (Eslami et al., 2015).”
Peeking Beneath the Hood of Uber (2015, background): “[7,8] examined user perceptions of algorithmically curated content in the Facebook News Feed.”