Background: Crowdsourced health research studies are the nexus of three contemporary trends: 1) citizen science (non-professionally trained individuals conducting science-related activities); 2) crowdsourcing (use of web-based technologies to recruit project participants); and 3) medicine 2.0 / health 2.0 (active participation of individuals in their health care particularly using web 2.0 technologies). Crowdsourced health research studies have arisen as a natural extension of the activities of health social networks (online health interest communities), and can be researcher-organized or participant-organized. In the last few years, professional researchers have been crowdsourcing cohorts from health social networks for the conduct of traditional studies. Participants have also begun to organize their own research studies through health social networks and health collaboration communities created especially for the purpose of self-experimentation and the investigation of health-related concerns. Objective: The objective of this analysis is to undertake a comprehensive narrative review of crowdsourced health research studies. This review will assess the status, impact, and prospects of crowdsourced health research studies. Methods: Crowdsourced health research studies were identified through a search of literature published from 2000 to 2011 and informal interviews conducted 2008-2011. Keyword terms related to crowdsourcing were sought in Medline/PubMed. Papers that presented results from human health studies that included crowdsourced populations were selected for inclusion. Crowdsourced health research studies not published in the scientific literature were identified by attending industry conferences and events, interviewing attendees, and reviewing related websites. Results: Participatory health is a growing area with individuals using health social networks, crowdsourced studies, smartphone health applications, and personal health records to achieve positive outcomes for a variety of health conditions. PatientsLikeMe and 23andMe are the leading operators of researcher-organized, crowdsourced health research studies. These operators have published findings in the areas of disease research, drug response, user experience in crowdsourced studies, and genetic association. Quantified Self, Genomera, and DIYgenomics are communities of participant-organized health research studies where individuals conduct self-experimentation and group studies. Crowdsourced health research studies have a diversity of intended outcomes and levels of scientific rigor. Conclusions: Participatory health initiatives are becoming part of the public health ecosystem and their rapid growth is facilitated by Internet and social networking influences. Large-scale parameter-stratified cohorts have potential to facilitate a next-generation understanding of disease and drug response. Not only is the large size of crowdsourced cohorts an asset to medical discovery, too is the near-immediate speed at which medical findings might be tested and applied. Participatory health initiatives are expanding the scope of medicine from a traditional focus on disease cure to a personalized preventive approach. Crowdsourced health research studies are a promising complement and extension to traditional clinical trials as a model for the conduct of health research.
Peer Production in Citizen Science: A Community-Centered Approach on the Example of Personal Science
Citizen science encompasses a wide range of practices where online collaboration for knowledge production plays a significant role. However, the study of forms of online collaboration other than crowdsourcing in citizen science has remained largely unexplored. This thesis aims to fill this gap by investigating peer production as a form of collaboration in online citizen science communities of practice. First, peer production theory was operationalized as a working model and used to analyze collaboration in citizen science case studies. This was followed by a comprehensive participatory design process for a specific use case involving the personal science community of practice. This process resulted in the creation of the “Personal Science Wiki”, an online space for consolidating community knowledge through peer production. Subsequently, a usability and card sorting study identified and resolved issues with the wiki implementation, and provided insights into mental models and content requirements regarding self-research knowledge. The lessons learned from the participatory design process were generalized as process recommendations for designing peer production solutions and knowledge management systems with communities of practice.
Social Uses of Personal Health Information Within PatientsLikeMe, an Online Patient Community: What Can Happen When Patients Have Access to One Another’s Data
Background: This project investigates the ways in which patients respond to the shared use of what is often considered private information: personal health data. There is a growing demand for patient access to personal health records. The predominant model for this record is a repository of all clinically relevant health information kept securely and viewed privately by patients and their health care providers. While this type of record does seem to have beneficial effects for the patient–physician relationship, the complexity and novelty of these data coupled with the lack of research in this area means the utility of personal health information for the primary stakeholders—the patients—is not well documented or understood. Objective: PatientsLikeMe is an online community built to support information exchange between patients. The site provides customized disease-specific outcome and visualization tools to help patients understand and share information about their condition. We begin this paper by describing the components and design of the online community. We then identify and analyze how users of this platform reference personal health information within patient-to-patient dialogues. Methods: Patients diagnosed with amyotrophic lateral sclerosis (ALS) post data on their current treatments, symptoms, and outcomes. These data are displayed graphically within personal health profiles and are reflected in composite community-level symptom and treatment reports. Users review and discuss these data within the Forum, private messaging, and comments posted on each other’s profiles. We analyzed member communications that referenced individual-level personal health data to determine how patient peers use personal health information within patient-to-patient exchanges. Results: Qualitative analysis of a sample of 123 comments (about 2% of the total) posted within the community revealed a variety of commenting and questioning behaviors by patient members. Members referenced data to locate others with particular experiences to answer specific health-related questions, to proffer personally acquired disease-management knowledge to those most likely to benefit from it, and to foster and solidify relationships based on shared concerns. Conclusions: Few studies examine the use of personal health information by patients themselves. This project suggests how patients who choose to explicitly share health data within a community may benefit from the process, helping them engage in dialogues that may inform disease self-management. We recommend that future designs make each patient’s health information as clear as possible, automate matching of people with similar conditions and using similar treatments, and integrate data into online platforms for health conversations.
Open Social Network Cookbook
The Open Social Network Cookbook is the collective product of the Open Social Incubator, a cohort that MEDLab hosted from Fall 2024 to Spring 2025. At the heart of our work together was the question: How can the online communities that we participate in use tools that truly reflect our values?
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.
Beyond Community Notes: A Framework for Understanding and Building Crowdsourced Context Systems for Social Media
Social media platforms are increasingly adopting features that display crowdsourced context alongside posts, a technique pioneered by X's Community Notes. These systems -- which we term Crowdsourced Context Systems (CCS) -- have the potential to reshape the information ecosystem as major platforms embrace them as alternatives to professional fact-checking. To understand the features and implications of these systems, we conduct a systematic literature review of existing CCS research (n=56) and analyze real-world CCS implementations. Based on our analysis, we develop a framework with two components. First, we present a theoretical model to conceptualize and define CCS. Second, we identify a design space encompassing six aspects: participation, inputs, curation, presentation, platform treatment, and transparency. We also surface normative implications of different CCS design and implementation choices. Our work integrates theoretical, design, and ethical perspectives to establish a foundation for future human-centered research on Crowdsourced Context Systems.
From Social Network to Sense Making
NLnet; Nanoarguments
Scientific knowledge is currently scattered across papers, repositories, and disconnected platforms, with no structured way to trace how claims connect to evidence or how arguments develop. Nanoarguments builds a framework and tools for creating, browsing, and contributing to a global, federated graph of scientific discourse and evidence. Researchers and their communities can collaboratively structure claims, evidence chains, and discussion as nanopublications, which are small, cryptographically signed Linked Data snippets with precise provenance and authorship, published to a decentralized peer-to-peer network. The project builds upon the Nanodash interface to help users browse, edit, and aggregate discourse and evidence graphs, and integrates with dokieli to enable in-context authoring of nanopublications as inline annotations while reading or writing a document. A bidirectional ActivityPub connector bridges the nanopublication network and the fediverse, allowing discourse threads to start as social exchanges and crystallize into persistent, machine-readable evidence records. The project will be piloted with early adopter research groups in discourse and evidence modeling. All components will be released as open-source modules that other systems can build upon.
How and When to Involve Crowds in Scientific Research - The Book
This book explores how millions of people can significantly contribute to scientific research. Discover main elements, authors, and who this book is for and why.
Two Research Papers Published on PatientsLikeMe - SPM Blog
Two research papers were published this month on the Health 2.0 website, PatientsLikeMe. PatientsLikeMe is arguably the only “real” health social network online today, because it lets patients share actual […]
Metagov x Future of Science Seminar - Discourse Graphs with Matt Akamatsu
How public involvement can improve the science of AI
As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls have included demands from affected parties to be given a seat at the table of AI evaluation. What, if anything, can public involvement add to the science of AI? In this perspective, we summarize the sociotechnical challenge of evaluating AI systems, which often adapt to multiple layers of social context that shape their outcomes. We then offer guidance for improving the science of AI by engaging lived-experience experts in the design, data collection, and interpretation of scientific evaluations. This article reviews common models of public engagement in AI research alongside common concerns about participatory methods, including questions about generalizable knowledge, subjectivity, reliability, and practical logistics. To address these questions, we summarize the literature on participatory science, discuss case studies from AI in healthcare, and share our own experience evaluating AI in areas from policing systems to social media algorithms. Overall, we describe five parts of any quantitative evaluation where public participation can improve the science of AI: equipoise, explanation, measurement, inference, and interpretation. We conclude with reflections on the role that participatory science can play in trustworthy AI by supporting trustworthy science.
Beyond APIs: Collecting Web Data for Research using the National Internet Observatory
Widespread Internet use offers unprecedented opportunities to study human behavior at scale, yet researchers face significant ethical and technical barriers when attempting to collect data for academic studies.
Empowering science communities with open, democratic, researcher-owned infrastructure.
We’re building communities and tech for publishing, curating, sharing, and discussing research online using ATProto and other decentralized protocols.
Empowering science communities with open, democratic, researcher-owned infrastructure.
We’re building communities and tech for publishing, curating, sharing, and discussing research online using ATProto and other decentralized protocols.
New study finds that when people help collect data or contribute to research it can build public trust by making scientists feel personally familiar and approachable, and that trust then spreads to how local and tangible the research feels. jcom.sissa.it/article/pubid/JCOM_2506_2026_…
How can citizen science reduce psychological distance to science? Insights from three projects in contested environmental contexts
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