@article{Tian2026, 
author = {Yunci Tian and Pu Yan},
title = {Measuring literacy in the algorithmically infused society: A mixed methods study on social media algorithmic advertising},
year = {2026},
journal = {iLibrary},
keywords = {algorithmic literacy, social media, mixed methods, eye-tracking},
url = {https://www.sciopen.com/article/10.26599/ILIB.2026.9690002},
doi = {10.26599/ILIB.2026.9690002},
abstract = {This study addresses the challenges of information equality by investigating algorithmic literacy and the capacity to understand and navigate recommendation algorithms. It responds directly to the core concern of building an algorithmic society by examining how users perceive and identify algorithmically generated social media ADs. A mixed-methods design was used in this study. A nationwide survey (N = 645) assessed internet use and algorithmic literacy, followed by an eye-tracking experiment (N = 43, Tobii Pro Fusion) that recorded users’ AD identification behavior in simulated social media feeds, providing behavioral validation of the survey findings. Quantitative data from both eye-tracking metrics and the survey were statistically analyzed using R. This included correlation tests and regression modeling to identify the key factors and pathways influencing the ability to recognize ADs. The analysis revealed that algorithmic awareness and age were significant factors in the ability to identify algorithmic advertisements. Furthermore, proficiency in internet skills was identified as a crucial factor for enhancing algorithmic awareness. The findings provide crucial user insights and empirical evidence for the design of a fairer and more transparent algorithmic system and provide strategies for improving algorithmic literacy education that could bridge the digital divide. This is essential to foster an equitable and digitally enlightened society.}
}