Content creators on social media compete against each other for algorithmic recommendations. Whether they produce for profit or leisure, increased exposure and engagement positively affect their utility. Despite the abundance of both content and consumers, the number of slots on each individual's feed are ultimately finite, and sophisticated algorithms use sophisticated engagement predictions to place content. In this paper, I present an analytical framework for understanding how consumer interests and algorithmic sorting influence the types of content produced on social media platforms. Building off of a Downsian framework, I model two producers who adjust the content they create in order to maximize their reach, given the production point of their competitor. Unlike typical Downsian models, social media engagement can come both from preferences being very close to content, or very far, what I term concordant and discordant engagement, respectively. I show that polarization of content production can occur with a sufficient prevalence of discordant engagement, even without polarization in the population or producer preferences. I support this finding with observational data and interviews with content creators, including media staffers for Members of Congress.

The content on social media platforms is created almost entirely by users. While much research has focused on how social media content affects users who consume it, it is equally important to understand how platforms and experiences on them affect content producers. Prior research suggests that engagement and platform monetization impact the frequency and type of content that users create. However, this research has typically avoided political content and forums. In this paper, I investigate how a costly engagement signal, Reddit Awards, affects both the production of comments and original posts, in both political and non-political subreddits. I find that Reddit Awards seem to incentivize increased comments for new users, but do little to move veteran Redditors. I find weak evidence for a relationship in the opposite direction for individuals who post, rather than comment. These results suggest that engagement can affect certain types of original content production, including political content. However, posting and commenting are different behaviors that appear to have distinct relationships with engagement and user tenure.

In the early phases of social media, content was a reverse-chronological stream of posts made by selected online connections. Now, all major social media platforms are increasingly moving toward algorithmically-curated content, drawing from a wider pool and sorting to maximize user engagement and retention. While the selected tweets or videos may seem like they fell out of a coconut tree, each item is strategically selected based on a user's activity, and that of all users who came before them. In this paper, I present results from two experiments on TikTok. In the first, I conduct an algorithmic audit to show how engagement signals can alter initial recommendations. I find that effect sizes are conditional on the topic of interest, noting that engagement with political content appears to trigger relatively high rate of related recommendations. In the second experiment, I examine how initial engagement signals persist over time. In the lab, I observe algorithmic behavior over 40 minutes of browsing by treatment-blind users. I find that political recommendations persist for treated accounts, even after significant browsing time. I also present preliminary results for algorithmic effects on user attitudes and experiences.