The growing influence of algorithmic systems in everyday digital environments has led to increasing scholarly interest in users’ awareness of these systems, a phenomenon commonly referred to in the literature as algorithmic awareness. In recent years, social science research has increasingly relied on survey questionnaires to investigate how individuals perceive, understand, and evaluate algorithmic systems.
This paper reviews how questionnaires have been employed in empirical research on algorithmic awareness, focusing on the methodological challenges associated with measuring this concept through survey research. Measuring algorithmic awareness poses specific difficulties for social research, as algorithmic systems are often opaque, operate in the background of everyday digital environments, and are only partially understood by users. As a result, individuals’ perceptions of their awareness may not correspond to their actual understanding of how algorithmic systems function.
Against this background, the paper examines the main measurement strategies adopted in the existing empirical and methodological literature. First, it reviews the use of self-report items that ask respondents to directly assess their level of awareness. Although widely adopted in studies on algorithmic awareness (Gran et al., 2021; Min, 2019; Oeldorf-Hirsch & Neubaum, 2023), these measures raise methodological concerns related to overconfidence, social desirability, and discrepancies between perceived and actual knowledge (Hargittai, 2009; Mondak, 1999). Second, the paper discusses psychometric scales developed to capture the multidimensional nature of algorithmic awareness, such as the Algorithmic Media Content Awareness scale (Zarouali et al., 2021), highlighting both their advantages and limitations. Third, it considers alternative survey designs aimed at measuring knowledge more indirectly, for instance through knowledge tests or experimental designs embedded within surveys (Brodsky et al., 2020; Cotter & Reisdorf, 2020).
Particular emphasis is placed on scenario-based questions, which present respondents with realistic situations involving algorithmic systems in everyday contexts such as social media, online shopping, and information search. This approach is particularly valuable when studying algorithmic awareness, as the concept itself is highly abstract and often difficult for respondents to evaluate directly. By grounding questions in concrete situations, scenario-based designs make the phenomenon more tangible and enable researchers to better capture individuals’ practical understanding of how algorithmic systems operate (Dogruel et al., 2022). Overall, the paper discusses the strengths and limitations of survey-based approaches and outlines methodological directions for future research on algorithmic awareness.
