The annotated bibliography was a helpful start to the research and writing process for my literature review. It got me started in the research and gave me a point from which to branch out.

Annotated Bibliography. Research Question:  How does individual activity impact algorithmic outcomes, and how do those algorithmic outcomes then impact individuals?

The dangers of the rabbit hole: Reflections on social media as a portal into a distorted world of edited bodies and eating disorder risk and the role of algorithms.

            Harriger, J. A., Evans, J. A., Thompson, J. K., & Tylka, T. L. (2022). The dangers of the rabbit hole: Reflections on social media as a portal into a distorted world of edited bodies and eating disorder risk and the role of algorithms. Body Image, volume  41, 292–297. https://doi.org/10.1016/j.bodyim.2022.03.007

            This article explores how social media algorithms intensify body dissatisfaction and eating disorder risk by continuously exposing users (especially adolescents and young adults) to idealized and edited images of bodies. The authors argue that while social media has long been linked to negative body image, the use of personalized algorithms deepens this problem by creating a “rabbit hole” effect, where users are guided toward increasingly extreme and appearance-focused content. They emphasize that although individuals, educators, and parents can promote media literacy and healthier digital habits, the ultimate responsibility lies with social media corporations, which must increase transparency, regulate algorithmic practices, and prioritize user well-being over engagement and profit.

This source connects to my topic regarding the effect algorithms have on individuals after those individuals have already been influenced by them. In this case, the individuals are young, vulnerable teenagers.

Influence of Facebook algorithms on political polarization tested

Garcia, D. (2023). Influence of Facebook algorithms on political polarization tested. Nature, 620, 39–41. https://doi-org.ccny-proxy1.libr.ccny.cuny.edu/10.1038/d41586-023-02325-x

Garcia reports on a series of large-scale experiments conducted through a collaboration between Meta Platforms (owner of Facebook/Instagram) and independent researchers during the U.S. 2020 election period. The study tested how changes to the Facebook News Feed algorithm would affect users’ political attitudes and polarization. Three significant interventions were tried: down-ranking content from like-minded sources, removing reshared content, and switching to reverse-chronological ordering of content. The article concludes that while algorithmic reform may help reduce harmful content, it is not sufficient by itself to reduce political polarization; structural, societal, and contextual factors must also be addressed. 

I will use this article to report on the political factors to account for when discussing social media’s algorithm influences on individuals or groups and vice versa.

No Targets, Just Vibes: Tuned Advertising and the Algorithmic Flow of Social Media

Brown, M. G. (2024). Tuned Advertising and the Algorithmic Flow of Social Media. Social Media + Society. Advance online publication. https://doi.org/10.1177/20563051241234691

Brown and colleagues argue that digital social-media advertising has moved beyond traditional “targeted advertising” toward what they call “tuned advertising” a dynamic process in which algorithms continuously optimize ads in real time to match a user’s evolving mood, behaviors, feed context, content flow and aesthetic “vibe.” The authors base this on a study of 204 young people in Victoria, Australia, who submitted over 5,169 screenshots of ads they encountered in their social-media feeds. They identify different “vibes” present in those ad collections, showing how the algorithmic system tunes not just which ad is shown, but how the ad fits into the broader stream of content, moods, images and rhythms of scrolling. The article argues that this “flow” of tuned advertising matters because it helps shape the user’s experience of social media, not simply through ad impressions, but as a continuous background stream that modulates attention, self-presentation, consumption practices, and subjectivity. 

This article can contribute to the marketing side of my research question, I would like to discuss individual marketing as it is happening now. 

Want to be on the top? Algorithmic power and the threat of invisibility on Facebook

Bucher, T. (2012). Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media & Society, 14(7). https://doi.org/10.1177/1461444812440159

In this article, examines how Facebook’s News Feed algorithm (commonly known as EdgeRank at the time) shapes user visibility, participation, and emotional experiences on the platform. It argues that algorithms don’t just organize content, they actively govern users by determining who gets seen and who becomes invisible. Bucher shows that people often feel anxious or pressured when they suspect the algorithm is hiding them or showing their posts to fewer people. This fear of algorithmic invisibility pushes users to modify their behavior, and they start posting more often, seeking likes, interacting strategically, or trying to “game” the system to stay visible.

I found that this article perfectly portrayed the anxiety people feel to post and stay active, which counts as algorithm affecting individuals. 

Social Drivers and Algorithmic Mechanisms on Digital Media

Metzler, H., & Garcia, D. (2023). Social drivers and algorithmic mechanisms on digital media. Perspectives on Psychological Science, 19(5). https://doi.org/10.1177/17456916231185057

Metzler and Garcia examine how digital-media algorithms interact with fundamental human social motives, such as the need for connection, status, and belonging. These then shape online behavior, mental health, and political dynamics. The authors argue that algorithms do not operate independently; instead, they amplify pre-existing social drivers by rewarding content that attracts attention, emotional reactions, and engagement. This creates feedback loops in which users modify their behavior to fit what the algorithm rewards, often intensifying social comparison, visibility pressures, and group-based conflicts. They conclude that to address the harms of digital platforms, society must consider not only algorithmic design but also broader social contexts.

This article contributes to the individuals affecting the algorithm sort of subconsciously since the reality might be that the algorithm rewards what society rewards.