Internet posts that get a lot of views play to an audience and are promoted by service providers

Internet posts that get lots of views generally do so because they fit what the audience is already primed to react to, and because platform algorithms are explicitly tuned to amplify that kind of engagement.pmc.ncbi.nlm.nih+2​

How platforms “play to an audience”

Social media ranking systems are typically optimized for engagement signals like clicks, likes, comments, and shares, treating those as a proxy for what users “want.” This setup means content that is emotionally charged, identity-affirming, or morally framed tends to outperform more neutral material, since it elicits stronger and faster reactions.knightcolumbia+3​

Studies of major platforms find that political and news content expressing anger, group identity, and out‑group hostility is systematically up-ranked compared to calmer or more nuanced posts. Creators adapt to this incentive structure over time, learning to shape messages toward outrage, tribal cues, and simplified narratives because these formats are reliably rewarded with reach.news.northwestern+2​

How providers promote high‑engagement content

Feed algorithms select and rank posts at scale, constantly testing what keeps each user on the platform longest and iterating toward those patterns. Research shows this process oversupplies “PRIME” information – prestigious, ingroup, moral, and emotional cues – because those characteristics exploit human social learning biases and drive more engagement.bipartisanpolicy+3​

Platform-side endorsement (e.g., recommendation slots, “For You” feeds, trending lists) can be as or more influential than purely social signals, as it exposes favored content to large audiences that would not have seen it otherwise. Since advertising revenue is tied to attention, providers have strong commercial incentives to keep promoting whatever maximizes engagement, even if it is polarizing or unrepresentative of what users would prefer in a less gamed environment.centerconflictcooperation-newsletter+3​

1. https://pmc.ncbi.nlm.nih.gov/articles/PMC11894805/
2. https://knightcolumbia.org/content/engagement-user-satisfaction-and-the-amplification-of-divisive-content-on-social-media
3. https://news.northwestern.edu/stories/2023/08/social-media-algorithms-exploit-how-humans-learn-from-their-peers
4. https://library.queens.edu/misinformation-on-social-media/algorithms
5. https://blog.routledge.com/social-sciences/are-social-media-algorithms-too-powerful/
6. https://bipartisanpolicy.org/wp-content/uploads/2023/10/BPC_Tech-Algorithm-Tradeoffs_R01.pdf
7. https://www.centerconflictcooperation-newsletter.com/p/changing-the-incentive-structure
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