Human social sensing is an untapped resource for computational social science

Manoel Horta Ribeiro (@manoelhortaribeiro.bsky.social)

Assistant Professor @ Princeton Previously: EPFL 🇨🇭, UFMG 🇧🇷 Interests: Computational Social Science, Platforms, GenAI, Moderation

AI Behavioral Science

We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop techniques for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to assess and infer AI's behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, and predicting human behaviors that we outline and discuss. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to understand the implications for the resulting economic and political outcomes. We outline issues that are increasingly pressing concerning the future of human-AI interactions and potential changes and disruptions that can ensue.

How Online Mobs Act Like Flocks Of Birds

A growing body of research suggests human behavior on social media is strikingly similar to collective behavior in nature.

AI for identifying social norm violation

Identifying social norms and their violation is a challenge facing several projects in computational science. This paper presents a novel approach to identifying social norm violations. We used GPT-3, zero-shot classification, and automatic rule discovery to develop simple predictive models grounded in psychological knowledge. Tested on two massive datasets, the models present significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools.

Stewardship of global collective behavior

Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a “crisis discipline” just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

Field Theory: AI as Social Science Question, Object & Tool

Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.

Governing Together: Toward Infrastructure for Community-Run Social Media | Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

Collaborative and social computing theory, concepts and paradigms

Bridging Social Movements and the New Social Web (working draft) - Variable Context with @CaptainCalliope.blue

On connecting movements, media, and new protocol ecosystems to rebuild the civic, cultural, and technical foundations of a freer, more self-determining society.

Socialized Proof of Work — Open Indie

There’s an increasing obsession with “humanness” these days: Humanness in the age of AI Worldcoin: a solution in search of its problem (...

Creating Scientific Theories with Online Communities using Gut Instinct

People's lived experiences provide intuitions about their health. Can they transform these personal intuitions into scientific theories that inform both science and their lives? My research introduces social computing architectures and system principles for people to brainstorm and test causal scientific theories. These ideas are instantiated in the Gut Instinct system (gutinstinct.ucsd.edu). 344 voluntary online participants from 27 countries created 399 personally-relevant questions about the human microbiome, 75 (19%) of which microbiome experts found potentially scientifically novel. To test their theories, end users design structurally-sound experiments, improve them via community reviews, and run them with other participants. Controlled experiments show that participants create better hypotheses and experimental designs when they have access to procedural training. My research illustrates a novel way to tackle complex, creative tasks online by building expertise in online volunteer communities.

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."

Socially Minded Intelligence: How Individuals, Groups, and Artificial Intelligence Can Make Each Other Smarter (or Not)

A core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically construe this either as an individual-difference trait or as a property of groups. We argue that by focusing either on individual or collective intelligence without considering their dynamic interaction, existing conceptualizations of intelligence limit the potential of people and AI systems. To address this impasse, we propose a new kind of intelligence, 'socially minded intelligence', that can be applied to both individuals and collectives. We outline how socially minded intelligence might be measured and cultivated within people, how it might be modelled in AI agents, and how it might be applied to other intelligent systems.