Abstract
Algorithms are widely used in our data-driven media landscape. Many misconceptions have arisen about how these algorithms work and what they can do. In this study, we conducted a large representative survey (<em>N</em> = 2,106) in the Netherlands to explore algorithmic misconceptions. Results showed that a significant part of the general population holds (multiple) misconceptions about algorithms in the media. We found that erroneous beliefs about algorithms are more common among (1) older people (vs. younger people), (2) lower-educated people (vs. higher-educated), and (3) women (vs. men). In addition, it was found that people who had no specific sources to inform themselves about algorithms, and those relying on their friends/family for information, were more likely to have algorithmic misconceptions. Conversely, media channels, school, and having one’s own (online) experiences were found to be sources associated with having fewer algorithmic misconceptions. Theoretical implications are formulated in the context of algorithmic awareness and the digital divide. Finally, societal implications are discussed, such as the need for algorithmic literacy initiatives.
Highlights
In our data‐driven media landscape, algorithms play an increasingly important role in how online users use, navi‐ gate, and consume online information and communica‐ tion (Beer, 2017; Lee, 2018; Ricci, 2015)
Drawing on the theoretical tenets of algo‐ rithmic awareness, we present findings from a large representative survey (N = 2,106) in the Netherlands in which we explore the prevalence of various miscon‐ ceptions about algorithms and their distribution among demographic groups, as well as mapping out the main information sources related to these misconceptions
This study showed that misconceptions about algo‐ rithms in the media are highly prevalent among the general population in the Netherlands
Summary
In our data‐driven media landscape, algorithms play an increasingly important role in how online users use, navi‐ gate, and consume online information and communica‐ tion (Beer, 2017; Lee, 2018; Ricci, 2015). Recommendation algorithms allow online platforms and legacy media alike to make personalized recommenda‐ tions based on people’s profiles; content moderation algorithms are used to determine the ranking of the contents that are being shown to us; automated filter‐ ing algorithms allow us to detect instances of misinfor‐ mation, harmful, or unlawful content; etc. Given their widespread use and impact on people’s media and infor‐.
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