Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed

By Emilee J. Rader, Rebecca Gray. 2015

395 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Safety Research

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Citations in the corpus

Manipulating and Measuring Model Interpretability (2018, background): “ent [33], judicial sentencing and bail [3, 14, 35, 37], and hiring [43]. In addition, these models inﬂuence decisions about what news people see on social media platforms (e.g., Twitter and Facebook, [2, 10, 48, 61]), provide recommendations for what movies to watch (e.g., Netﬂix, [7]) or what music”
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “…influence every facet of our lives, including the quality of healthcare and education we receive [16, 31, 32, 49], which news or social media posts we see [4, 18, 84], who receives a job [46, 82], who is released from jail [8, 23], and who is subjected to increased policing [65, 71, 94].”
How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface (2016, background): “Yet many users are unaware of this algorithm [7] or develop personal beliefs about it [22].”
"Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media (2017, background): “, [5, 10, 11, 29]) have focused on operational theories, defining algorithmic awareness as starting at what we call operational folk theories.”
Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms (2018, neutral): “These theories are often incomplete, are generated out of each user’s personal experiences, and serve to help them both to filter information and to make decisions about how they interact with the world (Gelman and Legare, 2011; Jones et al., 2011; Rader and Gray, 2015).”
Fake news and ideological polarization (2017, background): “One study investigating user beliefs around the Facebook News Feed found that most users are not particularly aware of the algorithm behind it (Rader and Gray, 2015, pp. 177–178).”
Ethics of the Attention Economy: The Problem of Social Media Addiction (2020, background): “For an overview of philosophical theories of intention, see Setiya (2018). periods of time (Lanier, 2018; Rader & Gray, 2015).11 The algorithms do this by monitoring the amount of time particular kinds of content keep the particular user engaged with the platform, and they use that data to…”
Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance (2021, background): “Rader and Gray argue that “because user behavior is both input for algorithms and constrained by them, these patterns of belief may have tangible consequences for the system as a whole” [64].”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, methodology): “Users may become aware of an algorithm through repeated experiences with a system [46].”
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).”
"Happy and Assured that life will be easy 10years from now.": Perceptions of Artificial Intelligence in 8 Countries (2019, background): “Qualitative work has also explored public perception of algorithmic systems, for example, nding that perception of algorithmic systems can vary substantially by individual factors as well as platform [15], and that end users often have fundamental questions or misconceptions about technical details ”
Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media (2021, methodology): “Previous work has used various methods to study users’ algorithmic literacy, from survey questionnaires (Cotter & Reisdorf, 2020; Gran et al., 2020; Rader & Gray, 2015) and experiments (Eslami et al., 2015) to focus groups (Siles et al., 2019) and in-depth interviews, often aided by card sorts…”
The Illusion of Control: Placebo Effects of Control Settings (2018, background): “Many researchers have explored sensemaking around social media and news feeds [7, 12, 18, 20, 48].”
A Qualitative Exploration of Perceptions of Algorithmic Fairness (2018, background): “For example, studies have found that some participants are not aware of algorithmic curation in the Facebook News Feed [31,69] or the gathering of online behavioral data and its use for inferencing [86], or underestimate the prevalence and scale of data gathering and its use in practical…”
The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms (2017, background): “As Rader and Gray (2015) point out, the feedback-loop characteristics of these systems make user beliefs an important component in shaping the overall system behaviour.”
Does Too Much News on Social Media Discourage News Seeking? Mediating Role of News Efficacy Between Perceived News Overload and News Avoidance on Social Media (2019, background): “Algorithms display subsets of a corpus of information for consumption by prioritizing, classifying, associating, and filtering information (Eslami et al., 2015; Rader & Gray, 2015).”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “In order to find out how an intelligent system should explain its behavior for non-experts, research on machine learning explanations has studied how users interpret intelligent agents [47, 164] and algorithms [171] to find out what users expect from machine explanations.”
Crossmod: A Cross-Community Learning-based System to Assist Reddit Moderators (2019, background): “Most online platforms today curate content generated by their users in different ways—presenting content in an ordered form to increase user engagement (e.g., Facebook NewsFeed [56]), promoting sponsored content (e.g., ads [44]), and moderating (or screening) undesirable content [33, 41, 63].”