Strategic Expression, Popularity Traps, and Welfare in Social Media

TL;DR

This paper models how social media incentivizes strategic expression for popularity, revealing its effects on polarization, welfare, and the emergence of popularity traps, with implications for platform algorithms and user well-being.

Contribution

It introduces popularity as a distinct strategic mechanism and develops a utilitarian welfare framework linked to observable platform metrics.

Findings

Popularity amplifies welfare in light topics but worsens it in polarized events.

Strategic expression increases polarization during polarized events and reduces it during unified events.

Homophilic algorithms mitigate the popularity trap and its negative welfare impacts.

Abstract

Social media platforms systematically reward popularity over authenticity, incentivizing users to strategically tailor their expression for attention. In this paper, we introduce (i) popularity as a strategic expression mechanism, distinct from the canonical mechanisms of conformity, learning, persuasion, and (mis)information transmission in social networks, and (ii) a utilitarian framework for measuring user welfare that maps directly to observable platform metrics, filling a critical gap in the social media literature. In the model, agents hold fixed heterogeneous authentic opinions and derive utility gains from the popularity of their own posts -- measured by likes received, and utility gains (losses) from exposure to content that aligns with (diverges from) their authentic opinion. Social media interaction acts as a state-dependent welfare amplifier: light topics generate Pareto improvements, whereas intense topics make everyone worse off in a polarized society (e.g., political debates during elections). Moreover, strategic expression amplifies social media polarization during polarized events while dampening it during unified events (e.g., national celebrations). Consequently, strategic distortions magnify welfare outcomes, expanding aggregate gains in light topics while exacerbating losses in intense, polarized ones. Counterintuitively, strategic agents often face a popularity trap: posting a more popular opinion is individually optimal, yet collective action by similar agents eliminates their authentic opinion from the platform, leaving them worse off than under the authentic-expression benchmark. Homophilic algorithms that match users with preferred content -- widely used by platforms -- discipline popularity-driven behavior, narrowing the popularity trap region and limiting its welfare effects.