Abstract
Online media platforms are increasingly using algorithms to select and present relevant information to their audiences. This highlights the importance of exploring whether people are aware of algorithmic content recommendations. Although some studies have already investigated algorithmic awareness, no standardized instrument has been developed yet to assess this construct. In this study, we therefore developed and validated the Algorithmic Media Content Awareness Scale (AMCA-scale). This scale contains four underlying dimensions: 1) users’ awareness of content filtering, 2) users’ awareness of automated decision-making, 3) users’ awareness of human-algorithm interplay, and 4) users’ awareness of ethical considerations. In validating the scale, results revealed strong psychometrics properties. The AMCA-scale was also successfully tested for three different online platforms: Facebook, YouTube, and Netflix, showing its robustness over different environments. Based on these findings, we conclude that the AMCA-scale offers scholars a valid, reliable and robust tool to measure algorithmic awareness.
Highlights
Algorithms are increasingly being utilized in a large number of industries, including our contemporary data-driven media land scape (Lee, 2018)
Studies have shown that people are often characterized by a lack of awareness of algorithmic content curation (e.g., Cotter & Reisdorf, 2020; Eslami et al, 2015; Powers, 2017). This filtering awareness plays an important role in changing users’ orientations toward and behaviors on the online platform (Bucher, 2017). Based on this line of reasoning, we argue that algorithmic awareness starts with a fundamental conception that algorithms are used to filter online content
The purpose of this study is to develop an instrument to measure the construct of Algorithmic Media Content Awareness (AMCA), based on the a priori set of five relevant dimensions that were identified based on a close inspection of the literature
Summary
Algorithms are increasingly being utilized in a large number of industries, including our contemporary data-driven media land scape (Lee, 2018). As a reliable and robust instrument, the AMCA-scale allows re searchers to compare people’s algorithmic awareness between studies and between contexts with respect to their similarities and differences By this means, we contribute to the accurate and reliable measurement of users’ algorithmic perceptions, which may lead to more valuable contributions to scientific theory, public policy and societal debate about the interplay between algorithms and users in a social platformed society. Some examples of mediated environments that use algorithms are: filtered newsfeed posts on social media platforms, filtered product offerings on e-commerce websites, filtered video overview on streaming platforms, filtered search engine results, etc These examples have different dynamics from a technical point-of-view, they do share the common tactic of algorithmic content curation, i.e., the use of algorithms (in some way) to select and presents relevant subsets of a large corpus of content to users (Rader & Gray, 2015).
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