Crowdsourcing is a socially distributed problem-solving and production tool that leverages collective intelligence for specific purposes. Crowdsourcing is “socially distributed” in that it requires participation from large numbers of people and because it can be used to understand the way crowds from different parts of society are similar or dissimilar, including the way crowds of adolescents might come to agree on certain ideas or phenomena (e.g., Top 40 radio stations) differently than do crowds of their elders (e.g., classic rock and “oldies” radio stations).
Crowdsourcing “leverages collective intelligence” in that crowds can sometimes be “smarter” than individuals. Returning to the radio example, the size of the crowd that tunes in and the amount spent on music ultimately determines the balance of radio bandwidth allocated to different music genres (although record producers may protest). This entry describes the crowdsourcing concept, discusses the way crowd members contribute to and are affected by the rapid rise of crowdsourcing, and closes with considerations on current and future directions for the use of crowdsourcing tools in popular society.
Crowdsourcing can be thought of as the process through which the results of many “micro-tasks” are transformed into a product or solution that is emergent, meaning that the results of a crowdsourcing project represent something greater than the additive sum of its (relatively tiny) parts. Crowdsourcing requires that individuals complete a specific task as part of a crowd, but this does not mean that individual crowd members must be aware of the myriad ways their completion of the task could inform crowdsourced solutions in the future.
Posting a “like” on YouTube or completing a “reCaptcha” while on a website are examples of large groups of people completing a well-defined task that produces a crowdsourced solution. The problem being solved, however, may not either be obvious or of interest to most individual members of the crowd (e.g., the use of reCaptcha responses to digitize antique books one word at a time).
Although crowdsourcing can be reduced to an informational resource tool, extracted from crowds for some specific purpose by organizations, government agencies, scientists, or others, crowdsourcing is also an extension of a long tradition in science focused on the study of social norms and human culture more generally. In this regard, crowdsourcing represents a kind of social measurement tool for the study of generative human culture (i.e., the processes through which ideas and social norms emerge, spread, and evolve among groups of people over the life course). A useful example is the use of crowdsourcing to measure the spread of cultural trends through social networks.
Well before the concept of an Internet meme was popularized as an image or other bit of media content that rapidly spreads through a social network, often with a humorous and concise caption, scientists coined the term meme to refer to a fundamental unit of cultural information that spreads and evolves within groups over time. Both meme types involve the contagious, sometimes viral and mutating, spread of an idea, yet only the Internet meme is actively revised and reshared by individuals who understand their connection to the wider conversation reverberating through cyberspace. Thus, social media users contribute to the continuous evolution of collectively shared meaning, while each individual member is simultaneously influenced by the normative trends that emerge from the network.
The science of crowdsourcing is inextricably linked to the science of crowds and crowd behavior, distributed within cities or across the Internet, and for this reason, crowdsourcing yields the greatest societal benefit when individual members of the crowd agree to volunteer and otherwise actively participate in the process. Crowd member involvement with the implementation of crowdsourcing tools is not just a matter of logistics; rather, active participation is required to go beyond simple observation of trends within large datasets.
Only the crowd knows what the crowd cares about and thus deems an important issue, and it follows that crowdsourcing is as useful for identifying problems as it is for solving them. In this sense, crowdsourcing is a means unto itself, not necessarily requiring the guidance of an outside entity pursuing its own agenda. Nevertheless, crowdsourcing is a tool that must be implemented by someone, and thus the crowd must rely on representative(s) who can integrate the disparate sources of information they believe might reveal a useful solution.
What does the crowd care about? Consider the cases of natural disasters, volunteer work, and charitable donations, as happens when a community that finds itself facing an uncertain set of circumstances coalesces around a new shared reality and in which the seemingly independent actions of many independent citizens eventually converge on a set of shared beliefs about how to move forward collectively. People who donate money or time prefer to give to worthy causes affecting their community or others less fortunate, but the amount they give is balanced against the cost.
A noteworthy feature of modern crowdsourcing systems is that the economic costs of participation approach zero, even as the costs of lost privacy remain an important consideration. Whether they realize or not, most people voluntarily “donate” their geographic location data as they drive around in cars, because doing so is the only way to benefit from phone-based street-navigation applications, all of which use movement data from large networks of users to identify traffic conditions and thereby identify optimized travel routes. This kind of volunteered geographic information is essential during a natural disaster, along with photographs and other data that can be crowdsourced from otherwise unreachable locations and that could prove transformational for disaster response systems of the future.
As people continue to accept trade-offs of this sort, conceding data about themselves in exchange for the undeniable conveniences of the rapidly emerging Internet of things, society faces a slippery slope. It is no secret that military and law enforcement agencies, as well as marketing firms and for-profit companies, realize huge value from the momentary traces people leave as they move through their activities of daily living.
Less clear is the potential for previously unimagined benefits that citizens create for themselves as they become comfortable with the idea that they can take control and contribute to the well-being of their community by simply donating data about their own day-to-day experiences. It is only a matter of time before communities realize the tremendous power they have to leverage the informational value of their own data for good—for example, communities crowdsourcing their own creative solutions to improve their own transit, public safety, parks and recreation, and food systems.
There is growing consensus about what appears to be the tremendous potential of emerging big data analytics, but questions remain about how to leverage the amazing amount of data available, especially data created within urban ecosystems, for societal benefit. Participatory crowdsourcing, sometimes referred to as “citizen science,” is rapidly paving the way for a new science of cities and health, yet precision measurement tools capable of systematically documenting everyday urban dynamics, such as the way resources flow between neighborhoods, are only beginning to be developed.
Crowdsourced data sharing make it possible to measure and study the way neighborhoods expand and contract as individuals, ideas, goods, and services are imported and exported over each hour of each day, with far-reaching implications for the study of human behavior and public health. The geographic, time-specific, connection between individuals and their location is also a real-time connection between citizens and the huge amount of data collected every moment in their cities.
Members of the crowd can “plug in” to disparate sources of big data on city systems in a way that can then be leveraged to provide smart, location-aware information, as well as additional opportunities to participate in the process. What can be done with such data? A primary purpose would be to uncover the fundamental mechanisms driving neighborhood health disparities, including the way neighborhood dynamics are associated with hospital readmission rates, differential exposure to airborne particulate matter, and so on.
The advent of 3D printing and other do-it-yourself production and shipping technologies will increasingly empower the crowd to produce valuable goods and services. Wikipedia is expanding collective intelligence, the OpenStreetMap community maps every corner of the globe under an open-source licensing framework, and the Galaxy Zoo community explores the visible universe.
Considered alongside other components of the sharing economy—such as ride sharing (e.g., Uber), home and workplace sharing (e.g., AirBNB), video entertainment and educational sharing (e.g., YouTube; n.b., the original home of Kahn Academy), and crowdfunding start-up ideas (e.g., Shark Tank)—the powerfully disruptive potential of crowdsourcing technologies would be hard to overstate. Demand from the marketplace always rewards quality production at lower cost, meaning that opportunities to crowdsource new products and ideas for solving societal challenges are likely to grow rapidly.
The microscope was developed during the 17th century, some 300 years ago, ushering in a new era of scientific inquiry, wherein scientists could directly observe phenomena that had previously been invisible. Deciphering the inner workings of cellular function continues to this day, including neuroimaging technologies that make it possible to navigate the geography of the brain and observe the neural circuitry that underlies all human experience.
Crowdsourcing represents a new kind of measurement and production tool that can leverage the social capital inherent to both virtually and geographically distributed groups of people. Bringing crowdsourced data together with data on geographically distributed societal systems offers the opportunity to enrich understanding of individual and community dynamics in a way that could revolutionize intervention implementation and the design of new health promotional policies into the future.
See also Big Data; Cooperation; Culture; Decision-Making; Health (Attitudes, Disparities, Promotion); Measurement; Metacognition; Online Communication; Social Networks; Sociometric Techniques
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