Algorithmic Literacy and Digital Privacy in the US: An Exploratory Study Using Data Visualization
This article investigates how algorithmic literacy empowers social media users to control their personal information. Based on secondary data analysis and visualization, this article explores algorithmic bias and digital privacy using the results of the American Trend Panel (ATP) surveys provided by the Pew Research Center ([1], [2]). In this article, a set of clear and organized visualizations is used to study digital privacy and the awareness of algorithmic bias. This article also conducts further analyses to explore the correlation between users' digital privacy scores and their awareness of privacy policies. Based on the findings, participants with higher digital literacy are more likely to intentionally influence or change the content they see on their Facebook news feed. In addition, the better respondents understand the mechanisms of algorithmic-driven content selection, they are more likely to report having control on social media content, whereas respondents with lower score on digital privacy tend to believe they have great control over their personal data collected by the government.
Authors
- Tyler Horan (ORCID: https://orcid.org/0000-0001-8535-5510)
- Haijing Tu
- Rahul Devajji
Institutions
- Amherst College (US)
- University of Massachusetts Amherst (US)
- Indiana University Bloomington (US)
- Indiana State University (US)
Publication Details
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1109/icad65464.2025.11114060
- Primary Topic
- Information Retrieval and Data Mining
- Type
- article
- Field-Weighted Citation Impact
- 0.00