Algorithm-Mediated Social Learning in Online Social Networks
Bibliographic record
Abstract
Humans have always relied on social learning to navigate the social and physical world. But for the first time in history, we are interacting in online social networks where content algorithms filter social information. Little is known about how these algorithms influence our social learning. In this review, we synthesize emerging insights into this ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We argue that the functions of human social learning and the goals of content algorithms are misaligned in practice. Algorithms exploit basic human social learning biases (i.e., a bias toward PRestigious, Ingroup, Moral and Emotional information, or PRIME information) as a side effect of their goals to sustain attention and maximize engagement on platforms. Social learning biases function to promote adaptive behaviors that foster cooperation and collective problem-solving. However, when social learning biases are exploited by algorithms, PRIME information saturates the digital social environment in ways that produce social misperceptions that are associated with conflict and misinformation. We show how this problem is ultimately driven by human-algorithm feedback loops where observational and reinforcement learning exacerbate algorithmic amplification, and how it may impact cultural evolution. Finally, we discuss practical solutions for mitigating functional misalignment in human-algorithm interactions via strategies that help algorithms promote more diverse and contextually-sensitive information environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".