Algorithm-Mediated Social Learning in Online Social Networks

Abstract

Humans rely heavily on social learning to navigate the social and physical world. For the first time in history, we are interacting in online social networks where content algorithms filter social information, yet 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 becomes amplified in the digital social environment in ways that can stimulate conflict and spread misinformation. We show how this problem is ultimately driven by human-algorithm interactions where observational and reinforcement learning exacerbate algorithmic amplification, and how it may even escalate to impact cultural evolution. Finally, we discuss practical solutions for reducing functional misalignment in human-algorithm interactions via strategies that help algorithms promote more diverse and contextually-sensitive information environments.

Extremely Partisan Samples Distort Perceptions of Group Beliefs

“…Scholars have noted the importance of increasing the transparency of content algorithms (e.g., those amplifying polarizing content), suggesting that increased transparency can mitigate social misperceptions by allowing individuals to adjust their inferences to account for biases in content selection (Brady et al, 2023). Mirroring such a scenario in which content selection biases are well-described, the present work demonstrates the potential benefits of algorithm transparency.…”

“…Taken together, the present work reveals one mechanism (i.e., insufficient adjustment from biased samples) that can explain how biased samples of political expressions, such as those commonly encountered online (Brady et al, 2023), lead people to misperceive the political beliefs of others. First, hidden biases in sample selection, such as those that amplify the voices of extreme partisans, foster partisan misperceptions as individuals fail to realize that the political opinions they encounter do not represent the beliefs held within a target group.…”

Generalized morality culturally evolves as an adaptive heuristic in large social networks.

“…To explain the recent trend toward affective polarization, different models have pointed to increasing political segregation (Motyl et al, 2014), the rise of politically polarized media (Martin & Yurukoglu, 2017), and the growing alignment of social identities (e.g., White Evangelical Christian) and political party membership (e.g., Republican; Mason, 2018). Predictions from our theory suggest that the rise of online social networks may also be a factor in the rise of affective polarization (Brady et al, 2023).…”

Disinformation elicits learning biases

“…We conclude by noting previous research has often attributed the negative impacts of disinformation, such as polarization and the formation of echo chambers, to intricate processes facilitated by external or self-selection of information [61][62][63] . These processes include algorithms tailoring information to align with users' attitudes 64 or individuals consciously opting to engage with like-minded peers 65 . However, our study reveals a more profound effect of disinformation, namely that even in minimal conditions, when low credibility information is explicitly identified, disinformation significantly impacts individuals' beliefs and decision-making processes.…”