For two decades, "social media" has been the master lens through which scholars have understood digital communication—an era defined by user-generated content, networked publics, and participatory culture. That era is drawing to a close. This paper argues that three interrelated dynamics are dissolving the social media paradigm: an algorithmic shift from social-graph-based to interest-based recommendation, which is remaking the active "user" into a passive "viewer"; the generative AI revolution, which is replacing user-generated content with synthetic media and decoupling platforms from any dependence on human participation; and an exodus from public platforms toward private, closed spaces. Together, these dynamics are giving rise to three distinct post-social formations: algorithmically governed broadcasting platforms, semi-private spheres and micro-communities, and AI-mediated communication as a new media form in its own right. Understanding this transformation demands a fundamental reorientation of the field—new conceptual tools that move beyond networked publics and participatory culture, new methods suited to synthetic and ephemeral media environments, and renewed attention to the political stakes of communication systems that no longer require human participation.
Towards a Post-Social Media Studies
For two decades, "social media" has been the master lens through which scholars have understood digital communication—an era defined by user-generated content, networked publics, and participatory culture. That era is drawing to a close. This paper argues that three interrelated dynamics are dissolving the social media paradigm: an algorithmic shift from social-graph-based to interest-based recommendation, which is remaking the active "user" into a passive "viewer"; the generative AI revolution, which is replacing user-generated content with synthetic media and decoupling platforms from any dependence on human participation; and an exodus from public platforms toward private, closed spaces. Together, these dynamics are giving rise to three distinct post-social formations: algorithmically governed broadcasting platforms, semi-private spheres and micro-communities, and AI-mediated communication as a new media form in its own right. Understanding this transformation demands a fundamental reorientation of the field—new conceptual tools that move beyond networked publics and participatory culture, new methods suited to synthetic and ephemeral media environments, and renewed attention to the political stakes of communication systems that no longer require human participation.
Shifts in U.S. Social Media Use, 2020–2024: Decline, Fragmentation, and Enduring Polarization
The social media ecosystem appears to be in flux. Twitter’s rebranding to X has come to symbolize a broader reorganization of online publics – political reshuffling, user attrition, and uncertainty over the role of legacy platforms. Simultaneously, the shift from text-based, networked feeds toward algorithmically curated, short-form video has accelerated, with TikTok setting the pace for the wider ecosystem [18, 14, 16]. At the same time, everyday communication increasingly migrates from large, open networks to semi-private spaces such as group chats and messaging apps. Commentators describe a digital public sphere in transition: smaller, more fragmented, less dominated by traditional social networking sites, and rise of more broadcast-oriented forms of media – with some even suggesting that we are seeing the beginning of the end of the social media era.
Social Media Is Now Parasocial Media
When practitioners used the term “social media” to describe the internet tools that emerged in the mid-aughts, they were giving a name to the kinds of platforms and protocols that allowed people to socialize with friends and communities of interest by using digital technologies. Twenty years later, users of social media are far more likely to scroll than post – and the content that they consume is often strategically produced and algorithmically curated. In this essay, I argue that the very essence of social media has changed. To more effectively interrogate what we are witnessing, we need to stop presuming that these tools are “social media” and begin recognizing that they are now “parasocial media.” Doing so raises new questions about digitally mediated sociality, not to mention the politics and governance of these platforms.
New_ Public | After the Feed
What happens after the social media feed? A presentation on the future of conversation, community, and social connection, and how AI will affect the everyday lives of billions.
Rational Silence and False Polarization: How Viewpoint Organizations and Recommender Systems Distort the Expression of Public Opinion
Social media platforms are one of the most important domains in which artificial intelligence (AI) has already transformed the nature of economic and social interaction. AI enables the massive scale and highly personalized nature of online information sharing that we now take for granted. Extensive attention has been devoted to the polarization that social media platforms appear to facilitate. However, a key implication of the transformation we are experiencing due to these AI-powered platforms has received much less attention: how platforms impact what observers of online discourse come to believe about community views. These observers include policymakers and legislators, who look to social media to gauge the prospects for policy and legislative change, as well as developers of AI models trained on large-scale internet data, whose outputs may similarly reflect a distorted view of public opinion. In this paper, we present a nested game-theoretic model to show how observed online opinion is produced by the interaction of the decisions made by users about whether and with what rhetorical intensity to share their opinions on a platform, the efforts of viewpoint organizations (such as traditional media and advocacy organizations) that seek to encourage or discourage opinion-sharing online, and the operation of AI-powered recommender systems controlled by social media platforms. We show that signals from ideological viewpoint organizations encourage an increase in rhetorical intensity, leading to the rational silence of moderate users. This, in turn, creates a polarized impression of where average opinions lie. We also show that this observed polarization can also be amplified by recommender systems that, pursuant to a platform’s incentive to maximize engagement, encourage the formation of viewpoint communities online that end up seeing a skewed sample of opinion. Unlike existing models, these well-known online phenomena are not here attributed to distortion in the formation of opinions nor to the seeking out of like-minded others, but rather to the interaction of the incentives of users, viewpoint organizations, and platforms implementing recommender systems. In addition to showing how these interactions can play out in simulations, we also identify practical strategies platforms can implement, such as reducing exposure to signals from ideological viewpoint organizations and a tailored approach to content moderation.
AI and the Future of Digital Public Squares
Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.
Social Media Should Be Public Infrastructure (with Ben Cerveny)
How Instagram's moderation rules are made
Social media algorithms can be redesigned to bridge divides — here’s how
"It falls to both the tech companies that built these systems and an engaged public to create technologies designed for social cohesion."
Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation
Social media platforms have been widely linked to societal harms, including rising polarization and the erosion of constructive debate. Can these problems be mitigated through prosocial interventions? We address this question using a novel method - generative social simulation - that embeds Large Language Models within Agent-Based Models to create socially rich synthetic platforms. We create a minimal platform where agents can post, repost, and follow others. We find that the resulting following-networks reproduce three well-documented dysfunctions: (1) partisan echo chambers; (2) concentrated influence among a small elite; and (3) the amplification of polarized voices - creating a 'social media prism' that distorts political discourse. We test six proposed interventions, from chronological feeds to bridging algorithms, finding only modest improvements - and in some cases, worsened outcomes. These results suggest that core dysfunctions may be rooted in the feedback between reactive engagement and network growth, raising the possibility that meaningful reform will require rethinking the foundational dynamics of platform architecture.
Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation
Social media platforms have been widely linked to societal harms, including rising polarization and the erosion of constructive debate. Can these problems be mitigated through prosocial interventions? We address this question using a novel method - generative social simulation - that embeds Large Language Models within Agent-Based Models to create socially rich synthetic platforms. We create a minimal platform where agents can post, repost, and follow others. We find that the resulting following-networks reproduce three well-documented dysfunctions: (1) partisan echo chambers; (2) concentrated influence among a small elite; and (3) the amplification of polarized voices - creating a 'social media prism' that distorts political discourse. We test six proposed interventions, from chronological feeds to bridging algorithms, finding only modest improvements - and in some cases, worsened outcomes. These results suggest that core dysfunctions may be rooted in the feedback between reactive engagement and network growth, raising the possibility that meaningful reform will require rethinking the foundational dynamics of platform architecture.
Community by Design
Social media empower distributed content creation by algorithmically harnessing "the social fabric" (explicit and implicit signals of association) to serve this content. While this overcomes the bottlenecks and biases of traditional gatekeepers, many believe it has unsustainably eroded the very social fabric it depends on by maximizing engagement for advertising revenue. This paper participates in open and ongoing considerations to translate social and political values and conventions, specifically social cohesion, into platform design. We propose an alternative platform model that includes the social fabric an explicit output as well as input. Citizens are members of communities defined by explicit affiliation or clusters of shared attitudes. Both have internal divisions, as citizens are members of intersecting communities, which are themselves internally diverse. Each is understood to value content that bridge (viz. achieve consensus across) and balance (viz. represent fairly) this internal diversity, consistent with the principles of the Hutchins Commission (1947). Content is labeled with social provenance, indicating for which community or citizen it is bridging or balancing. Subscription payments allow citizens and communities to increase the algorithmic weight on the content they value in the content serving algorithm. Advertisers may, with consent of citizen or community counterparties, target them in exchange for payment or increase in that party's algorithmic weight. Underserved and emerging communities and citizens are optimally subsidized/supported to develop into paying participants. Content creators and communities that curate content are rewarded for their contributions with algorithmic weight and/or revenue. We discuss applications to productivity (e.g. LinkedIn), political (e.g. X), and cultural (e.g. TikTok) platforms.
Social Media Effects: Hijacking Democracy and Civility in Civic Engagement
Perceived as an equalizing force for disenfranchised individuals without a voice, the importance of social networks as agents of change cannot be ignored. However, in some societies, social networks have evolved into a platform for fake news and propaganda, empowering disruptive voices, ideologies, and messages. Social networks such as Twitter, Facebook, and Google hold the potential to alter civic engagement, thus essentially hijacking democracy, by influencing individuals toward a particular way of thinking.
After the feed with Eli Pariser, Ben Smith, and Jasmine Sun
Rewilding the internet with ATProto
So far, many of the projects in the ATProtocol ecosystem have focused on bringing parity—making projects that replicate our existing tools and platforms, but with the affordances of the protocol instead. That's cool and we need to do that, but I think we can go much farther, and take this opportunity to challenge some of the biggest assumptions of what the modern internet is for, who builds it, and how we interact with it. This talk is in conversation with several of the talks from last year, and in I'll argue that we'll never get away from skeuomorphism until we've fundamentally changed the definition of what "the social internet" means in the first place.
✨New paper out @nature.com ✨ For 8 weeks around the 2024 US election, we randomly assigned 2,000 people to use social media algos we built ourselves. Do engagement-based algorithms amplify intergroup, moral & emotional (IME) content—and does that distort how we see political norms? 🧵🔗 👇
