Algorithm Influence
Overview
Growing controversies surrounding data protection and shadowbanning, for example the TikTok hearings in Congress or the EU’s Digitial Services Act, have spotlighted the quiet but significant way in which algorithms become the arbiters of what we see, share, and believe. Understanding the ways in which certain content may be promoted or regulated by prevailing media algorithms can equip users to discern organic virality from artificial engagement. Platforms and their subsequent content are often times designed to maxmize engagement adobe all else. This results in surfacing content not for its accuracy or value, but for its ability it provoke, polarize, or perform. This section evaluates how susceptible a post or trend is to algorithmic amplification — highlighting the ways in which platform mechanics may distort visibility and impact, often without the user ever realizing.
Organic Origin: Entirely grassroots; shared through private or niche channels. Not shaped by algorithmic systems.
Mild Influence: Content surfaces occasionally in recommendations or trends but doesn’t rely on them for primary traction.
Mixed Distribution: Both organic and algorithmic forces are clearly at play. Engagement likely determines reach, but it wasn’t created for the algorithm.
Engineered Engagement: Content is designed with algorithmic systems in mind—clickbait, optimized posting times, or triggering engagement.
Algorithm-Dependent: The trend exists because of the algorithm. Without it, it would likely remain obscure or fail to circulate.
Research
Our understanding of algorithmic influence was shaped by a range of studies investigating how platform algorithms affect content visibility, virality, and perception. See below for citations
Eslami, Motahhare, et al. “Algorithmic Awareness in the Everyday Use of Facebook.” Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015, pp. 153–162. ACM Digital Library, doi:10.1145/2702123.2702556.
DeVito, Michael A. “From Editors to Algorithms: A Values-Based Approach to Understanding Story Selection in the Facebook News Feed.” Digital Journalism, vol. 5, no. 6, 2017, pp. 753–773, doi:10.1080/21670811.2016.1178592.
Cinelli, Matteo, et al. “The Echo Chamber Effect on Social Media.” Proceedings of the National Academy of Sciences, vol. 118, no. 9, 2021, doi:10.1073/pnas.2023301118.