New Research Network to Study and Design Innovative Ways of Solving Public Problems

Curated on Posted on by Stefaan Verhulst

Network

MacArthur Foundation Research Network on Opening Governance formed to gather evidence and develop new designs for governing 

NEW YORK, NY, March 4, 2014 – The Governance Lab (The GovLab) at New York University today announced the formation of a Research Network on Opening Governance, which will seek to develop blueprints for more effective and legitimate democratic institutions to help improve people’s lives.
Convened and organized by the GovLab, the MacArthur Foundation Research Network on Opening Governance is made possible by a three-year grant of $5 million from the John D. and Catherine T. MacArthur Foundation as well as a gift from Google.org, which will allow the Network to tap the latest technological advances to further its work.
Combining empirical research with real-world experiments, the Research Network will study what happens when governments and institutions open themselves to diverse participation, pursue collaborative problem-solving, and seek input and expertise from a range of people. Network members include twelve experts (see below) in computer science, political science, policy informatics, social psychology and philosophy, law, and communications. This core group is supported by an advisory network of academics, technologists, and current and former government officials. Together, they will assess existing innovations in governing and experiment with new practices and how institutions make decisions at the local, national, and international levels.
Support for the Network from Google.org will be used to build technology platforms to solve problems more openly and to run agile, real-world, empirical experiments with institutional partners such as governments and NGOs to discover what can enhance collaboration and decision-making in the public interest.
The Network’s research will be complemented by theoretical writing and compelling storytelling designed to articulate and demonstrate clearly and concretely how governing agencies might work better than they do today. “We want to arm policymakers and practitioners with evidence of what works and what does not,” says Professor Beth Simone Noveck, Network Chair and author of Wiki Government: How Technology Can Make Government Better, Democracy Stronger and Citi More Powerful, “which is vital to drive innovation, re-establish legitimacy and more effectively target scarce resources to solve today’s problems.”
“From prize-backed challenges to spur creative thinking to the use of expert networks to get the smartest people focused on a problem no matter where they work, this shift from top-down, closed, and professional government to decentralized, open, and smarter governance may be the major social innovation of the 21st century,” says Noveck. “The MacArthur Research Network on Opening Governance is the ideal crucible for helping  transition from closed and centralized to open and collaborative institutions of governance in a way that is scientifically sound and yields new insights to inform future efforts, always with an eye toward real-world impacts.”
MacArthur Foundation President Robert Gallucci added, “Recognizing that we cannot solve today’s challenges with yesterday’s tools, this interdisciplinary group will bring fresh thinking to questions about how our governing institutions operate, and how they can develop better ways to help address seemingly intractable social problems for the common good.”
Members
The MacArthur Research Network on Opening Governance comprises:
Chair: Beth Simone Noveck
Network Coordinator: Andrew Young
Chief of Research: Stefaan Verhulst
Faculty Members:

  • Sir Tim Berners-Lee (Massachusetts Institute of Technology (MIT)/University of Southampton, UK)
  • Deborah Estrin (Cornell Tech/Weill Cornell Medical College)
  • Erik Johnston (Arizona State University)
  • Henry Farrell (George Washington University)
  • Sheena S. Iyengar (Columbia Business School/Jerome A. Chazen Institute of International Business)
  • Karim Lakhani (Harvard Business School)
  • Anita McGahan (University of Toronto)
  • Cosma Shalizi (Carnegie Mellon/Santa Fe Institute)

Institutional Members:

  • Christian Bason and Jesper Christiansen (MindLab, Denmark)
  • Geoff Mulgan (National Endowment for Science Technology and the Arts – NESTA, United Kingdom)
  • Lee Rainie (Pew Research Center)

The Network is eager to hear from and engage with the public as it undertakes its work. Please contact Stefaan Verhulst to share your ideas or identify opportunities to collaborate.”

Coordinating the Commons: Diversity & Dynamics in Open Collaborations

Curated on Posted on by Stefaan Verhulst

Dissertation by Jonathan T. Morgan: “The success of Wikipedia demonstrates that open collaboration can be an effective model for organizing geographically-distributed volunteers to perform complex, sustained work at a massive scale. However, Wikipedia’s history also demonstrates some of the challenges that large, long-term open collaborations face: the core community of Wikipedia editors—the volunteers who contribute most of the encyclopedia’s content and ensure that articles are correct and consistent — has been gradually shrinking since 2007, in part because Wikipedia’s social climate has become increasingly inhospitable for newcomers, female editors, and editors from other underrepresented demographics. Previous research studies of change over time within other work contexts, such as corporations, suggests that incremental processes such as bureaucratic formalization can make organizations more rule-bound and less adaptable — in effect, less open— as they grow and age. There has been little research on how open collaborations like Wikipedia change over time, and on the impact of those changes on the social dynamics of the collaborating community and the way community members prioritize and perform work. Learning from Wikipedia’s successes and failures can help researchers and designers understand how to support open collaborations in other domains — such as Free/Libre Open Source Software, Citizen Science, and Citizen Journalism.

In this dissertation, I examine the role of openness, and the potential antecedents and consequences of formalization, within Wikipedia through an analysis of three distinct but interrelated social structures: community-created rules within the Wikipedia policy environment, coordination work and group dynamics within self-organized open teams called WikiProjects, and the socialization mechanisms that Wikipedia editors use to teach new community members how to participate.To inquire further, I have designed a new editor peer support space, the Wikipedia Teahouse, based on the findings from my empirical studies. The Teahouse is a volunteer-driven project that provides a welcoming and engaging environment in which new editors can learn how to be productive members of the Wikipedia community, with the goal of increasing the number and diversity of newcomers who go on to make substantial contributions to Wikipedia …”

The benefits—and limits—of decision models

Curated on Posted on by Stefaan Verhulst

Article by Phil Rosenzweig in McKinsey Quaterly: “The growing power of decision models has captured plenty of C-suite attention in recent years. Combining vast amounts of data and increasingly sophisticated algorithms, modeling has opened up new pathways for improving corporate performance.1 Models can be immensely useful, often making very accurate predictions or guiding knotty optimization choices and, in the process, can help companies to avoid some of the common biases that at times undermine leaders’ judgments.
Yet when organizations embrace decision models, they sometimes overlook the need to use them well. In this article, I’ll address an important distinction between outcomes leaders can influence and those they cannot. For things that executives cannot directly influence, accurate judgments are paramount and the new modeling tools can be valuable. However, when a senior manager can have a direct influence over the outcome of a decision, the challenge is quite different. In this case, the task isn’t to predict what will happen but to make it happen. Here, positive thinking—indeed, a healthy dose of management confidence—can make the difference between success and failure.

Where models work well

Examples of successful decision models are numerous and growing. Retailers gather real-time information about customer behavior by monitoring preferences and spending patterns. They can also run experiments to test the impact of changes in pricing or packaging and then rapidly observe the quantities sold. Banks approve loans and insurance companies extend coverage, basing their decisions on models that are continually updated, factoring in the most information to make the best decisions.
Some recent applications are truly dazzling. Certain companies analyze masses of financial transactions in real time to detect fraudulent credit-card use. A number of companies are gathering years of data about temperature and rainfall across the United States to run weather simulations and help farmers decide what to plant and when. Better risk management and improved crop yields are the result.
Other examples of decision models border on the humorous. Garth Sundem and John Tierney devised a model to shed light on what they described, tongues firmly in cheek, as one of the world’s great unsolved mysteries: how long will a celebrity marriage last? They came up with the Sundem/Tierney Unified Celebrity Theory, which predicted the length of a marriage based on the couple’s combined age (older was better), whether either had tied the knot before (failed marriages were not a good sign), and how long they had dated (the longer the better). The model also took into account fame (measured by hits on a Google search) and sex appeal (the share of those Google hits that came up with images of the wife scantily clad). With only a handful of variables, the model did a very good job of predicting the fate of celebrity marriages over the next few years.
Models have also shown remarkable power in fields that are usually considered the domain of experts. With data from France’s premier wine-producing regions, Bordeaux and Burgundy, Princeton economist Orley Ashenfelter devised a model that used just three variables to predict the quality of a vintage: winter rainfall, harvest rainfall, and average growing-season temperature. To the surprise of many, the model outperformed wine connoisseurs.
Why do decision models perform so well? In part because they can gather vast quantities of data, but also because they avoid common biases that undermine human judgment.2 People tend to be overly precise, believing that their estimates will be more accurate than they really are. They suffer from the recency bias, placing too much weight on the most immediate information. They are also unreliable: ask someone the same question on two different occasions and you may get two different answers. Decision models have none of these drawbacks; they weigh all data objectively and evenly. No wonder they do better than humans.

Can we control outcomes?

With so many impressive examples, we might conclude that decision models can improve just about anything. That would be a mistake. Executives need not only to appreciate the power of models but also to be cognizant of their limits.
Look back over the previous examples. In every case, the goal was to make a prediction about something that could not be influenced directly. Models can estimate whether a loan will be repaid but won’t actually change the likelihood that payments will arrive on time, give borrowers a greater capacity to pay, or make sure they don’t squander their money before payment is due. Models can predict the rainfall and days of sunshine on a given farm in central Iowa but can’t change the weather. They can estimate how long a celebrity marriage might last but won’t help it last longer or cause another to end sooner. They can predict the quality of a wine vintage but won’t make the wine any better, reduce its acidity, improve the balance, or change the undertones. For these sorts of estimates, finding ways to avoid bias and maintain accuracy is essential.
Executives, however, are not concerned only with predicting things they cannot influence. Their primary duty—as the word execution implies—is to get things done. The task of leadership is to mobilize people to achieve a desired end. For that, leaders need to inspire their followers to reach demanding goals, perhaps even to do more than they have done before or believe is possible. Here, positive thinking matters. Holding a somewhat exaggerated level of self-confidence isn’t a dangerous bias; it often helps to stimulate higher performance.
This distinction seems simple but it’s often overlooked. In our embrace of decision models, we sometimes forget that so much of life is about getting things done, not predicting things we cannot control.
…

Improving models over time

Part of the appeal of decision models lies in their ability to make predictions, to compare those predictions with what actually happens, and then to evolve so as to make more accurate predictions. In retailing, for example, companies can run experiments with different combinations of price and packaging, then rapidly obtain feedback and alter their marketing strategy. Netflix captures rapid feedback to learn what programs have the greatest appeal and then uses those insights to adjust its offerings. Models are not only useful at any particular moment but can also be updated over time to become more and more accurate.
Using feedback to improve models is a powerful technique but is more applicable in some settings than in others. Dynamic improvement depends on two features: first, the observation of results should not make any future occurrence either more or less likely and, second, the feedback cycle of observation and adjustment should happen rapidly. Both conditions hold in retailing, where customer behavior can be measured without directly altering it and results can be applied rapidly, with prices or other features changed almost in real time. They also hold in weather forecasting, since daily measurements can refine models and help to improve subsequent predictions. The steady improvement of models that predict weather—from an average error (in the maximum temperature) of 6 degrees Fahrenheit in the early 1970s to 5 degrees in the 1990s and just 4 by 2010—is testimony to the power of updated models.
For other events, however, these two conditions may not be present. As noted, executives not only estimate things they cannot affect but are also charged with bringing about outcomes. Some of the most consequential decisions of all—including the launch of a new product, entry into a new market, or the acquisition of a rival—are about mobilizing resources to get things done. Furthermore, the results are not immediately visible and may take months or years to unfold. The ability to gather and insert objective feedback into a model, to update it, and to make a better decision the next time just isn’t present.
None of these caveats call into question the considerable power of decision analysis and predictive models in so many domains. They help underscore the main point: an appreciation of decision analytics is important, but an understanding of when these techniques are useful and of their limitations is essential, too…”

Get Smart: Commission brings “open planning” movement to Europe to speed spread of smart cities

Curated on Posted on by Stefaan Verhulst

Press Release: “The European Commission is calling on those involved in creating smart cities to publish their efforts in order to help build an open planning movement from the ground up.
The challenge is being issued to city administrations, small and large companies and other organisations to go public with their ICT, energy and mobility plans, so that all parties can learn from each other and grow the smart city market. Through collaboration as well as traditional competition, the Europe will get smarter, more competitive and more sustainable.
The Commission is looking for both new commitments to “get smart” and for interested parties to share their current and past successes. Sharing these ideas will feed the European Innovation Partnership on Smart Cities and Communities (see IP/13/1159 and MEMO/13/1049) and networks such as the Smart Cities Stakeholder Platform, the Green Digital Charter, the Covenant of Mayors, and CIVITAS.
What’s in it for me?
If you are working in the smart cities field, joining the open planning movement will help you find the right partners, get better access to finance and make it easier to learn from your peers. You will help grow the marketplace you work in, and create export opportunities outside of Europe.
If you live in a city, you will benefit sooner from better traffic flows, greener buildings, and cheaper or more convenient services.
European Commission Vice President Neelie Kroes said, “For those of us living in cities, – we need to make sure they are smart cities. Nothing else makes sense. And nothing else is such a worldwide economic opportunity – so we need to get sharing!”.
Energy Commissioner Günther Oettinger said: “Cities and Communities can only get smart if mayors and governors are committed to apply innovative industrial solutions”.
In June 2014 the Commission will then seek to analyse, group and promote the best plans and initiatives.”

The Problem with Easy Technology

Curated on Posted on by Stefaan Verhulst

New post by at the NewYorker: “In the history of marketing, there’s a classic tale that centers on the humble cake mix. During the nineteen-fifties, there were differences of opinion over how “instant” powdered cake mixes should be, and, in particular, over whether adding an egg ought to be part of the process. The first cake mixes, invented in the nineteen-thirties, merely required water, and some people argued that this approach, the easiest, was best. But others thought bakers would want to do more. Urged on by marketing psychologists, Betty Crocker herself began to instruct housewives to “add water, and two of your own fresh eggs.”…
The choice between demanding and easy technologies may be crucial to what we have called technological evolution. We are, as I argued in my most recent piece in this series, self-evolving. We make ourselves into what we, as a species, will become, mainly through our choices as consumers. If you accept these premises, our choice of technological tools becomes all-important; by the logic of biological atrophy, our unused skills and capacities tend to melt away, like the tail of an ape. It may sound overly dramatic, but the use of demanding technologies may actually be important to the future of the human race.
Just what is a demanding technology? Three elements are defining: it is technology that takes time to master, whose usage is highly occupying, and whose operation includes some real risk of failure. By this measure, a piano is a demanding technology, as is a frying pan, a programming language, or a paintbrush. So-called convenience technologies, in contrast—like instant mashed potatoes or automatic transmissions—usually require little concentrated effort and yield predictable results.
There is much to be said for the convenience technologies that have remade human society over the past century. They often open up life’s pleasures to a wider range of people (downhill skiing, for example, can be exhausting without lifts). They also distribute technological power more widely: consider that, nowadays, you don’t need special skills to take pretty good photos, or to capture a video of police brutality. Nor should we neglect that promise first made to all Americans in the nineteen-thirties: freedom from a life of drudgery to focus on what we really care about. Life is hard enough; do we need to be churning our own butter? Convenience technologies promised more space in our lives for other things, like thought, reflection, and leisure.
That, at least, is the idea. But, even on its own terms, convenience technology has failed us. Take that promise of liberation from overwork. In 1964, Life magazine, in an article about “Too Much Leisure,” asserted that “there will certainly be a sharp decline in the average work week” and that “some prophets of what automation is doing to our economy think we are on the verge of a 30-hour week; others as low as 25 or 20.” Obviously, we blew it. Our technologies may have made us prosthetic gods, yet they have somehow failed to deliver on the central promise of free time. The problem is that, as every individual task becomes easier, we demand much more of both ourselves and others. Instead of fewer difficult tasks (writing several long letters) we are left with a larger volume of small tasks (writing hundreds of e-mails). We have become plagued by a tyranny of tiny tasks, individually simple but collectively oppressive. And, when every task in life is easy, there remains just one profession left: multitasking.
The risks of biological atrophy are even more important. Convenience technologies supposedly free us to focus on what matters, but sometimes the part that matters is what gets eliminated. Everyone knows that it is easier to drive to the top of a mountain than to hike; the views may be the same, but the feeling never is. By the same logic, we may evolve into creatures that can do more but find that what we do has somehow been robbed of the satisfaction we hoped it might contain.
The project of self-evolution demands an understanding of humanity’s relationship with tools, which is mysterious and defining. Some scientists, like the archaeologist Timothy Taylor, believe that our biological evolution was shaped by the tools our ancestors chose eons ago. Anecdotally, when people describe what matters to them, second only to human relationships is usually the mastery of some demanding tool. Playing the guitar, fishing, golfing, rock-climbing, sculpting, and painting all demand mastery of stubborn tools that often fail to do what we want. Perhaps the key to these and other demanding technologies is that they constantly require new learning. The brain is stimulated and forced to change. Conversely, when things are too easy, as a species we may become like unchallenged schoolchildren, sullen and perpetually dissatisfied.
I don’t mean to insist that everything need be done the hard way, or that we somehow need to suffer like our ancestors to achieve redemption. It isn’t somehow wrong to use a microwave rather than a wood fire to reheat leftovers. But we must take seriously our biological need to be challenged, or face the danger of evolving into creatures whose lives are more productive but also less satisfying.
There have always been groups, often outcasts, who have insisted on adhering to harder ways of doing some things. Compared to Camrys, motorcycles are unreliable, painful, and dangerous, yet some people cannot leave them alone. It may seem crazy to use command-line or plain-text editing software in an age of advanced user interfaces, but some people still do. In our times, D.I.Y. enthusiasts, hackers, and members of the maker movement are some of the people who intuitively understand the importance of demanding tools, without rejecting the idea that technology can improve the human condition. Derided for lacking a “political strategy,” they nonetheless realize that there are far more important agendas than the merely political. Whether they know it or not, they are trying to work out the future of what it means to be human, and, along the way, trying to find out how to make that existence worthwhile.”

Big Data, Big New Businesses

Curated on Posted on by Stefaan Verhulst

Nigel Shaboldt and Michael Chui: “Many people have long believed that if government and the private sector agreed to share their data more freely, and allow it to be processed using the right analytics, previously unimaginable solutions to countless social, economic, and commercial problems would emerge. They may have no idea how right they are.

Even the most vocal proponents of open data appear to have underestimated how many profitable ideas and businesses stand to be created. More than 40 governments worldwide have committed to opening up their electronic data – including weather records, crime statistics, transport information, and much more – to businesses, consumers, and the general public. The McKinsey Global Institute estimates that the annual value of open data in education, transportation, consumer products, electricity, oil and gas, health care, and consumer finance could reach $3 trillion.

These benefits come in the form of new and better goods and services, as well as efficiency savings for businesses, consumers, and citizens. The range is vast. For example, drawing on data from various government agencies, the Climate Corporation (recently bought for $1 billion) has taken 30 years of weather data, 60 years of data on crop yields, and 14 terabytes of information on soil types to create customized insurance products.

Similarly, real-time traffic and transit information can be accessed on smartphone apps to inform users when the next bus is coming or how to avoid traffic congestion. And, by analyzing online comments about their products, manufacturers can identify which features consumers are most willing to pay for, and develop their business and investment strategies accordingly.

Opportunities are everywhere. A raft of open-data start-ups are now being incubated at the London-based Open Data Institute (ODI), which focuses on improving our understanding of corporate ownership, health-care delivery, energy, finance, transport, and many other areas of public interest.

Consumers are the main beneficiaries, especially in the household-goods market. It is estimated that consumers making better-informed buying decisions across sectors could capture an estimated $1.1 trillion in value annually. Third-party data aggregators are already allowing customers to compare prices across online and brick-and-mortar shops. Many also permit customers to compare quality ratings, safety data (drawn, for example, from official injury reports), information about the provenance of food, and producers’ environmental and labor practices.

Consider the book industry. Bookstores once regarded their inventory as a trade secret. Customers, competitors, and even suppliers seldom knew what stock bookstores held. Nowadays, by contrast, bookstores not only report what stock they carry but also when customers’ orders will arrive. If they did not, they would be excluded from the product-aggregation sites that have come to determine so many buying decisions.

The health-care sector is a prime target for achieving new efficiencies. By sharing the treatment data of a large patient population, for example, care providers can better identify practices that could save $180 billion annually.

The Open Data Institute-backed start-up Mastodon C uses open data on doctors’ prescriptions to differentiate among expensive patent medicines and cheaper “off-patent” varieties; when applied to just one class of drug, that could save around $400 million in one year for the British National Health Service. Meanwhile, open data on acquired infections in British hospitals has led to the publication of hospital-performance tables, a major factor in the 85% drop in reported infections.

There are also opportunities to prevent lifestyle-related diseases and improve treatment by enabling patients to compare their own data with aggregated data on similar patients. This has been shown to motivate patients to improve their diet, exercise more often, and take their medicines regularly. Similarly, letting people compare their energy use with that of their peers could prompt them to save hundreds of billions of dollars in electricity costs each year, to say nothing of reducing carbon emissions.

Such benchmarking is even more valuable for businesses seeking to improve their operational efficiency. The oil and gas industry, for example, could save $450 billion annually by sharing anonymized and aggregated data on the management of upstream and downstream facilities.

Finally, the move toward open data serves a variety of socially desirable ends, ranging from the reuse of publicly funded research to support work on poverty, inclusion, or discrimination, to the disclosure by corporations such as Nike of their supply-chain data and environmental impact.

There are, of course, challenges arising from the proliferation and systematic use of open data. Companies fear for their intellectual property; ordinary citizens worry about how their private information might be used and abused. Last year, Telefónica, the world’s fifth-largest mobile-network provider, tried to allay such fears by launching a digital confidence program to reassure customers that innovations in transparency would be implemented responsibly and without compromising users’ personal information.

The sensitive handling of these issues will be essential if we are to reap the potential $3 trillion in value that usage of open data could deliver each year. Consumers, policymakers, and companies must work together, not just to agree on common standards of analysis, but also to set the ground rules for the protection of privacy and property.”

Visualising Information for Advocacy

Curated on Posted on by Stefaan Verhulst

New book: “Visualising Information for Advocacy is a book about how advocates and activists use visual elements in their campaigns. This 170-page guide features over 60 case studies from around the world to provide an introduction to understanding visual information and a framework for using images for influence.
At Tactical Tech we have been analysing how different kinds of visual techniques serve the work of advocacy, and have been testing out our ideas. We have developed three ways to classify how the visual works in advocacy campaigns:

  • Get the idea is about making simple, eye-catching products that convey one concise point, provoking and inviting audiences to find out more about the issue.
  • Get the picture is about creating a visual summary of an argument by crafting a narrative with visuals and data.
  • Get the detail is about presenting data through interactive digital formats in a way that allows the audience to dig deeper and explore the issue for themselves.

Flick through Visualising Information for Advocacy to get inspiration for your project, try out some of the visual techniques showcased, or find advice on how we produce visuals for advocates.”

Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters

Curated on Posted on by Stefaan Verhulst

Pew Internet: “Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation.
If a topic is political, it is common to see two separate, polarized crowds take shape. They form two distinct discussion groups that mostly do not interact with each other. Frequently these are recognizably liberal or conservative groups. The participants within each separate group commonly mention very different collections of website URLs and use distinct hashtags and words. The split is clearly evident in many highly controversial discussions: people in clusters that we identified as liberal used URLs for mainstream news websites, while groups we identified as conservative used links to conservative news websites and commentary sources. At the center of each group are discussion leaders, the prominent people who are widely replied to or mentioned in the discussion. In polarized discussions, each group links to a different set of influential people or organizations that can be found at the center of each conversation cluster.
While these polarized crowds are common in political conversations on Twitter, it is important to remember that the people who take the time to post and talk about political issues on Twitter are a special group. Unlike many other Twitter members, they pay attention to issues, politicians, and political news, so their conversations are not representative of the views of the full Twitterverse. Moreover, Twitter users are only 18% of internet users and 14% of the overall adult population. Their demographic profile is not reflective of the full population. Additionally, other work by the Pew Research Center has shown that tweeters’ reactions to events are often at odds with overall public opinion— sometimes being more liberal, but not always. Finally, forthcoming survey findings from Pew Research will explore the relatively modest size of the social networking population who exchange political content in their network.
Still, the structure of these Twitter conversations says something meaningful about political discourse these days and the tendency of politically active citizens to sort themselves into distinct partisan camps. Social networking maps of these conversations provide new insights because they combine analysis of the opinions people express on Twitter, the information sources they cite in their tweets, analysis of who is in the networks of the tweeters, and how big those networks are. And to the extent that these online conversations are followed by a broader audience, their impact may reach well beyond the participants themselves.
Our approach combines analysis of the size and structure of the network and its sub-groups with analysis of the words, hashtags and URLs people use. Each person who contributes to a Twitter conversation is located in a specific position in the web of relationships among all participants in the conversation. Some people occupy rare positions in the network that suggest that they have special importance and power in the conversation.
Social network maps of Twitter crowds and other collections of social media can be created with innovative data analysis tools that provide new insight into the landscape of social media. These maps highlight the people and topics that drive conversations and group behavior – insights that add to what can be learned from surveys or focus groups or even sentiment analysis of tweets. Maps of previously hidden landscapes of social media highlight the key people, groups, and topics being discussed.

Conversational archetypes on Twitter

The Polarized Crowd network structure is only one of several different ways that crowds and conversations can take shape on Twitter. There are at least six distinctive structures of social media crowds which form depending on the subject being discussed, the information sources being cited, the social networks of the people talking about the subject, and the leaders of the conversation. Each has a different social structure and shape: divided, unified, fragmented, clustered, and inward and outward hub and spokes.
After an analysis of many thousands of Twitter maps, we found six different kinds of network crowds.

Polarized Crowds in Twitter Conversations
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Polarized Crowd: Polarized discussions feature two big and dense groups that have little connection between them. The topics being discussed are often highly divisive and heated political subjects. In fact, there is usually little conversation between these groups despite the fact that they are focused on the same topic. Polarized Crowds on Twitter are not arguing. They are ignoring one another while pointing to different web resources and using different hashtags.
Why this matters: It shows that partisan Twitter users rely on different information sources. While liberals link to many mainstream news sources, conservatives link to a different set of websites.

Tight Crowds in Twitter Conversations
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Tight Crowd: These discussions are characterized by highly interconnected people with few isolated participants. Many conferences, professional topics, hobby groups, and other subjects that attract communities take this Tight Crowd form.
Why this matters: These structures show how networked learning communities function and how sharing and mutual support can be facilitated by social media.

Brand Clusters in Twitter Conversations
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Brand Clusters: When well-known products or services or popular subjects like celebrities are discussed in Twitter, there is often commentary from many disconnected participants: These “isolates” participating in a conversation cluster are on the left side of the picture on the left). Well-known brands and other popular subjects can attract large fragmented Twitter populations who tweet about it but not to each other. The larger the population talking about a brand, the less likely it is that participants are connected to one another. Brand-mentioning participants focus on a topic, but tend not to connect to each other.
Why this matters: There are still institutions and topics that command mass interest. Often times, the Twitter chatter about these institutions and their messages is not among people connecting with each other. Rather, they are relaying or passing along the message of the institution or person and there is no extra exchange of ideas.

Community Clusters in Twitter Conversations
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Community Clusters: Some popular topics may develop multiple smaller groups, which often form around a few hubs each with its own audience, influencers, and sources of information. These Community Clusters conversations look like bazaars with multiple centers of activity. Global news stories often attract coverage from many news outlets, each with its own following. That creates a collection of medium-sized groups—and a fair number of isolates (the left side of the picture above).
Why this matters: Some information sources and subjects ignite multiple conversations, each cultivating its own audience and community. These can illustrate diverse angles on a subject based on its relevance to different audiences, revealing a diversity of opinion and perspective on a social media topic.

Broadcast Networks in Twitter Conversations
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Broadcast Network: Twitter commentary around breaking news stories and the output of well-known media outlets and pundits has a distinctive hub and spoke structure in which many people repeat what prominent news and media organizations tweet. The members of the Broadcast Network audience are often connected only to the hub news source, without connecting to one another. In some cases there are smaller subgroups of densely connected people— think of them as subject groupies—who do discuss the news with one another.
Why this matters: There are still powerful agenda setters and conversation starters in the new social media world. Enterprises and personalities with loyal followings can still have a large impact on the conversation.

Support Networks in Twitter Conversations
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Support Network: Customer complaints for a major business are often handled by a Twitter service account that attempts to resolve and manage customer issues around their products and services. This produces a hub and spoke structure that is different from the Broadcast Network pattern. In the Support Network structure, the hub account replies to many otherwise disconnected users, creating outward spokes. In contrast, in the Broadcast pattern, the hub gets replied to or retweeted by many disconnected people, creating inward spokes.
Why this matters: As government, businesses, and groups increasingly provide services and support via social media, support network structures become an important benchmark for evaluating the performance of these institutions. Customer support streams of advice and feedback can be measured in terms of efficiency and reach using social media network maps.

Why is it useful to map the social landscape this way?

Social media is increasingly home to civil society, the place where knowledge sharing, public discussions, debates, and disputes are carried out. As the new public square, social media conversations are as important to document as any other large public gathering. Network maps of public social media discussions in services like Twitter can provide insights into the role social media plays in our society. These maps are like aerial photographs of a crowd, showing the rough size and composition of a population. These maps can be augmented with on the ground interviews with crowd participants, collecting their words and interests. Insights from network analysis and visualization can complement survey or focus group research methods and can enhance sentiment analysis of the text of messages like tweets.
Like topographic maps of mountain ranges, network maps can also illustrate the points on the landscape that have the highest elevation. Some people occupy locations in networks that are analogous to positions of strategic importance on the physical landscape. Network measures of “centrality” can identify key people in influential locations in the discussion network, highlighting the people leading the conversation. The content these people create is often the most popular and widely repeated in these networks, reflecting the significant role these people play in social media discussions.
While the physical world has been mapped in great detail, the social media landscape remains mostly unknown. However, the tools and techniques for social media mapping are improving, allowing more analysts to get social media data, analyze it, and contribute to the collective construction of a more complete map of the social media world. A more complete map and understanding of the social media landscape will help interpret the trends, topics, and implications of these new communication technologies.”

A Natural History of Human Thinking

Curated on Posted on by Stefaan Verhulst
New Harvard University Press Book: “Tool-making or culture, language or religious belief: ever since Darwin, thinkers have struggled to identify what fundamentally differentiates human beings from other animals. In this much-anticipated book, Michael Tomasello weaves his twenty years of comparative studies of humans and great apes into a compelling argument that cooperative social interaction is the key to our cognitive uniqueness. Once our ancestors learned to put their heads together with others to pursue shared goals, humankind was on an evolutionary path all its own.
Tomasello argues that our prehuman ancestors, like today’s great apes, were social beings who could solve problems by thinking. But they were almost entirely competitive, aiming only at their individual goals. As ecological changes forced them into more cooperative living arrangements, early humans had to coordinate their actions and communicate their thoughts with collaborative partners. Tomasello’s “shared intentionality hypothesis” captures how these more socially complex forms of life led to more conceptually complex forms of thinking. In order to survive, humans had to learn to see the world from multiple social perspectives, to draw socially recursive inferences, and to monitor their own thinking via the normative standards of the group. Even language and culture arose from the preexisting need to work together. What differentiates us most from other great apes, Tomasello proposes, are the new forms of thinking engendered by our new forms of collaborative and communicative interaction.
A Natural History of Human Thinking is the most detailed scientific analysis to date of the connection between human sociality and cognition.”

NatureNet: a model for crowdsourcing the design of citizen science systems

Curated on Posted on by Stefaan Verhulst

Paper in CSCW Companion ’14, the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing: “NatureNet is citizen science system designed for collecting bio-diversity data in nature park settings. Park visitors are encouraged to participate in the design of the system in addition to collecting bio-diversity data. Our goal is to increase the motivation to participate in citizen science via crowdsourcing: the hypothesis is that when the crowd plays a role in the design and development of the system, they become stakeholders in the project and work to ensure its success. This paper presents a model for crowdsourcing design and citizen science data collection, and the results from early trials with users that illustrate the potential of this approach.”