New Paper by A. Kornrumpf and U. Baumol in the International Journal of Cooperative Information Systems: “Collective intelligence (CI) has become a popular research topic over the past few years. However, the CI debate suffers from several problems such as that there is no unanimously agreed-upon definition of CI that clearly differentiates between CI and related terms such as swarm intelligence (SI) and collective intelligence systems (CIS). Furthermore, a model of such CIS is lacking for purposes of research and the design of new CIS. This paper aims at untangling the definitions of CI and other related terms, especially CIS, and at providing a semi-structured model of CIS as a first step towards more structured research. The authors of this paper argue that CI can be defined as the ability of sufficiently large groups of individuals to create an emergent solution for a specific class of problems or tasks. The authors show that other alleged properties of CI which are not covered by this definition, are, in fact, properties of CIS and can be understood by regarding CIS as complex socio-technical systems (STS) that enable the realization of CI. The model defined in this article serves as a means to structure open questions in CIS research and helps to understand which research methodology is adequate for different aspects of CIS.”
Towards an information systems perspective and research agenda on crowdsourcing for innovation
New paper by A Majchrzak and A Malhotra in The Journal of Strategic Information Systems: “Recent years have seen an increasing emphasis on open innovation by firms to keep pace with the growing intricacy of products and services and the ever changing needs of the markets. Much has been written about open innovation and its manifestation in the form of crowdsourcing. Unfortunately, most management research has taken the information system (IS) as a given. In this essay we contend that IS is not just an enabler but rather can be a shaper that optimizes open innovation in general and crowdsourcing in particular. This essay is intended to frame crowdsourcing for innovation in a manner that makes more apparent the issues that require research from an IS perspective. In doing so, we delineate the contributions that the IS field can make to the field of crowdsourcing.
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Reviews participation architectures supporting current crowdsourcing, finding them inadequate for innovation development by the crowd.
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Identifies 3 tensions for explaining why a participation architecture for crowdsourced innovation is difficult.
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Identifies affordances for the participation architectures that may help to manage the tension.
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Uses the tensions and possible affordances to identify research questions for IS scholars.”
The Value of Personal Data
The Digital Enlightenment Yearbook 2013 is dedicated this year to Personal Data: “The value of personal data has traditionally been understood in ethical terms as a safeguard for personality rights such as human dignity and privacy. However, we have entered an era where personal data are mined, traded and monetized in the process of creating added value – often in terms of free services including efficient search, support for social networking and personalized communications. This volume investigates whether the economic value of personal data can be realized without compromising privacy, fairness and contextual integrity. It brings scholars and scientists from the disciplines of computer science, law and social science together with policymakers, engineers and entrepreneurs with practical experience of implementing personal data management.
The resulting collection will be of interest to anyone concerned about privacy in our digital age, especially those working in the field of personal information management, whether academics, policymakers, or those working in the private sector.”
A Global Online Network Lets Health Professionals Share Expertise
Rebecca Weintraub, Aaron C. Beals, Sophie G. Beauvais, Marie Connelly, Julie Rosenberg Talbot, Aaron VanDerlip, and Keri Wachter in HBR Blog Network : “In response, our team at the Global Health Delivery Project at Harvard launched an online platform to generate and disseminate knowledge in health care delivery. With guidance from Paul English, chief technology officer of Kayak, we borrowed a common tool from business — professional virtual communities (PVCs) — and adapted it to leverage the wisdom of the crowds. In business, PVCs are used for knowledge management and exchange across multiple organizations, industries, and geographies. In health care, we thought, they could be a rapid, practical means for diverse professionals to share insights and tactics. As GHDonline’s rapid growth and success have demonstrated, they can indeed be a valuable tool for improving the efficiency, quality, and the ultimate value of health care delivery….
Creating a professional virtual network that would be high quality, participatory, and trusted required some trial and error both in terms of the content and technology. What features would make the site inviting, accessible, and useful? How could members establish trust? What would it take to involve professionals from differing time zones in different languages?
The team launched GHDonline in June 2008 with public communities in tuberculosis-infection control, drug-resistant tuberculosis, adherence and retention, and health information technology. Bowing to the reality of the sporadic electricity service and limited internet bandwidth available in many countries, we built a lightweight platform, meaning that the site minimized the use of images and only had features deemed essential….
Even with early successes in terms of membership growth and daily postings to communities, user feedback and analytics directed the team to simplify the user navigation and experience. Longer, more nuanced, in-depth conversations in the communities were turned into “discussion briefs” — two-page, moderator-reviewed summaries of the conversations. The GHDonline team integrated Google Translate to accommodate the growing number of non-native English speakers. New public communities were launched for nursing, surgery, and HIV and malaria treatment and prevention. You can view all of the features of GHDOnline here (PDF).”
Using Big Data to Ask Big Questions
Chase Davis in the SOURCE: “First, let’s dispense with the buzzwords. Big Data isn’t what you think it is: Every federal campaign contribution over the last 30-plus years amounts to several tens of millions of records. That’s not Big. Neither is a dataset of 50 million Medicare records. Or even 260 gigabytes of files related to offshore tax havens—at least not when Google counts its data in exabytes. No, the stuff we analyze in pursuit of journalism and app-building is downright tiny by comparison.
But you know what? That’s ok. Because while super-smart Silicon Valley PhDs are busy helping Facebook crunch through petabytes of user data, they’re also throwing off intellectual exhaust that we can benefit from in the journalism and civic data communities. Most notably: the ability to ask Big Questions.
Most of us who analyze public data for fun and profit are familiar with small questions. They’re focused, incisive, and often have the kind of black-and-white, definitive answers that end up in news stories: How much money did Barack Obama raise in 2012? Is the murder rate in my town going up or down?
Big Questions, on the other hand, are speculative, exploratory, and systemic. As the name implies, they are also answered at scale: Rather than distilling a small slice of a dataset into a concrete answer, Big Questions look at entire datasets and reveal small questions you wouldn’t have thought to ask.
Can we track individual campaign donor behavior over decades, and what does that tell us about their influence in politics? Which neighborhoods in my city are experiencing spikes in crime this week, and are police changing patrols accordingly?
Or, by way of example, how often do interest groups propose cookie-cutter bills in state legislatures?
Looking at Legislation
Even if you don’t follow politics, you probably won’t be shocked to learn that lawmakers don’t always write their own bills. In fact, interest groups sometimes write them word-for-word.
Sometimes those groups even try to push their bills in multiple states. The conservative American Legislative Exchange Council has gotten some press, but liberal groups, social and business interests, and even sororities and fraternities have done it too.
On its face, something about elected officials signing their names to cookie-cutter bills runs head-first against people’s ideal of deliberative Democracy—hence, it tends to make news. Those can be great stories, but they’re often limited in scope to a particular bill, politician, or interest group. They’re based on small questions.
Data science lets us expand our scope. Rather than focusing on one bill, or one interest group, or one state, why not ask: How many model bills were introduced in all 50 states, period, by anyone, during the last legislative session? No matter what they’re about. No matter who introduced them. No matter where they were introduced.
Now that’s a Big Question. And with some basic data science, it’s not particularly hard to answer—at least at a superficial level.
Analyze All the Things!
Just for kicks, I tried building a system to answer this question earlier this year. It was intended as an example, so I tried to choose methods that would make intuitive sense. But it also makes liberal use of techniques applied often to Big Data analysis: k-means clustering, matrices, graphs, and the like.
If you want to follow along, the code is here….
To make exploration a little easier, my code represents similar bills in graph space, shown at the top of this article. Each dot (known as a node) represents a bill. And a line connecting two bills (known as an edge) means they were sufficiently similar, according to my criteria (a cosine similarity of 0.75 or above). Thrown into a visualization software like Gephi, it’s easy to click around the clusters and see what pops out. So what do we find?
There are 375 clusters in total. Because of the limitations of our data, many of them represent vague, subject-specific bills that just happen to have similar titles even though the legislation itself is probably very different (think things like “Budget Bill” and “Campaign Finance Reform”). This is where having full bill text would come handy.
But mixed in with those bills are a handful of interesting nuggets. Several bills that appear to be modeled after legislation by the National Conference of Insurance Legislators appear in multiple states, among them: a bill related to limited lines travel insurance; another related to unclaimed insurance benefits; and one related to certificates of insurance.”
The Shutdown’s Data Blackout
Opinion piece by Katherine G. Abraham and John Haltiwanger in The New York Times: “Today, for the first time since 1996 and only the second time in modern memory, the Bureau of Labor Statistics will not issue its monthly jobs report, as a result of the shutdown of nonessential government services. This raises an important question: Are the B.L.S. report and other economic data that the government provides “nonessential”?
If we’re trying to understand how much damage the shutdown or sequestration cuts are doing to jobs or the fragile economic recovery, they are definitely essential. Without robust economic data from the federal government, we can speculate, but we won’t really know.
In the last two shutdowns, in 1995 and 1996, the Congressional Budget Office estimated the economic damage at around 0.5 percent of the gross domestic product. This time, Moody’s estimates that a three-to-four-week shutdown might subtract 1.4 percent (annualized) from gross domestic product growth this quarter and take $55 billion out of the economy. Democrats tend to play up such projections; Republicans tend to play them down. If the shutdown continues, though, we’ll all be less able to tell what impact it is having, because more reports like the B.L.S. jobs report will be delayed, while others may never be issued.
In fact, sequestration cuts that affected 2013 budgets are already leading federal statistics agencies to defer or discontinue dozens of reports on everything from income to overseas labor costs. The economic data these agencies produce are key to tracking G.D.P., earnings and jobs, and to informing the Federal Reserve, the executive branch and Congress on the state of the economy and the impact of economic policies. The data are also critical for decisions made by state and local policy makers, businesses and households.
The combined budget for all the federal statistics agencies totals less than 0.1 percent of the federal budget. Yet the same across-the-board-cut mentality that led to sequester and shutdown has shortsightedly cut statistics agencies, too, as if there were something “nonessential” about spending money on accurately assessing the economic effects of government actions and inactions. As a result, as we move through the shutdown, the debt-ceiling fight and beyond, reliable, essential data on the impact of policy decisions will be harder to come by.
Unless the sequester cuts are reversed, funding for economic data will shrink further in 2014, on top of a string of lean budget years. More data reports will be eliminated at the B.L.S., the Census Bureau, the Bureau of Economic Analysis and other agencies. Even more insidious damage will come from compromising the methods for producing the reports that still are paid for and from failing to prepare for the future.
To save money, survey sample sizes will be cut, reducing the reliability of national data and undermining local statistics. Fewer resources will be devoted to maintaining the listings used to draw business survey samples, running the risk that surveys based on those listings won’t do as good a job of capturing actual economic conditions. Hiring and training will be curtailed. Over time, the availability and quality of economic indicators will diminish.
That would be especially paradoxical and backward at a time when economic statistics can and should be advancing through technological innovation instead of marched backward by politics. Integrating survey data, administrative data and commercial data collected with scanners and other digital technologies could produce richer, more useful information with less of a burden on businesses and households.
Now more than ever, framing sound economic policy depends on timely and accurate information about the economy. Bad or ill-targeted data can lead to bad or ill-targeted decisions about taxes and spending. The tighter the budget and the more contentious the political debate around it, the more compelling the argument for investing in federal data that accurately show how government policies are affecting the economy, so we can target the most effective cuts or spending or other policies, and make ourselves accountable for their results. That’s why Congress should restore funding to the federal statistical agencies at a level that allows them to carry out their critical work.”
Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo
New paper by Michael J. Madison: “The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, some times known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.”
Are Some Tweets More Interesting Than Others? #HardQuestion
New paper by Microsoft Research (Omar Alonso, Catherine C. Marshall, and Marc Najork): “Twitter has evolved into a significant communication nexus, coupling personal and highly contextual utterances with local news, memes, celebrity gossip, headlines, and other microblogging subgenres. If we take Twitter as a large and varied dynamic collection, how can we predict which tweets will be interesting to a broad audience in advance of lagging social indicators of interest such as retweets? The telegraphic form of tweets, coupled with the subjective notion of interestingness, makes it difficult for human judges to agree on which tweets are indeed interesting.
In this paper, we address two questions: Can we develop a reliable strategy that results in high-quality labels for a collection of tweets, and can we use this labeled collection to predict a tweet’s interestingness?
To answer the first question, we performed a series of studies using crowdsourcing to reach a diverse set of workers who served as a proxy for an audience with variable interests and perspectives. This method allowed us to explore different labeling strategies, including varying the judges, the labels they applied, the datasets, and other aspects of the task.
To address the second question, we used crowdsourcing to assemble a set of tweets rated as interesting or not; we scored these tweets using textual and contextual features; and we used these scores as inputs to a binary classifier. We were able to achieve moderate agreement (kappa = 0.52) between the best classifier and the human assessments, a figure which reflects the challenges of the judgment task.”
Technology Can Expose Government Sins, But You Need Humans to Fix Them
Lorelei Kelly: “We can’t bring accountability to the NSA unless we figure out how to give the whole legislative branch modern methods for policy oversight. Those modern methods can include technology, but the primary requirement is figuring out how to supply Congress with unbiased subject matter experts—not just industry lobbyists or partisan think tank analysts. Why? Because trusted and available expertise inside the process of policymaking is what is missing today.
According to calculations by the Sunlight Foundation, today’s Congress is operating with about 40 percent less staff than in 1979. According to the Congressional Management Foundation, it’s also contending with at least 800 percent more incoming communications. Yet, instead of helping Congress gain insight in new ways, instead of helping it sort and filter, curate and authenticate, technology has mostly created disorganized information overload. And the information Congress receives is often sentiment, not substance. Elected leaders should pay attention to both, but need the latter for policymaking.
The result? Congress defaults to what it knows. And that means slapping a “national security” label on policy questions that instead deserve to be treated as broad public conversations about the evolution of American democracy. This is a Congress that categorizes questions about our freedoms on the Internet as “cyber security.”
What can we do? First, recognize that Congress is an obsolete and incapacitated system, and treat it as such. Technology and transparency can help modernize our legislature, but they can’t fix the system of governance.
Activists, even tech-savvy ones, need to talk directly with Congressional members and staff at home. Hackers, you should invite your representatives to wherever you do your hacking. And then offer your skills to help them in any way possible. You may create some great data maps and visualization tools, but the real point is to make friends in Congress. There’s no substitute for repeated conversations, and long-haul engagement. In politics, relationships will leverage the technology. All technology can do is help you find one another.
Without our help and our knowledge, our elected leaders and governing institutions won’t have the bandwidth to cope with our complex world. This will be a steep climb. But, like nearly every good outcome in politics, the climb starts with an outstretched hand, not one that’s poised at a keyboard, ready to tweet.”
How to Make All Apps More Civic
Nick Grossman in Idea Lab: “The big idea in all of this is that through open data and standards and API-based interoperability, it’s possible not just to build more “civic apps,” but to make all apps more civic:
So in a perfect world, I’d not only be able to get my transit information from anywhere (say, Citymapper), I’d be able to read restaurant inspection data from anywhere (say, Foursquare), be able to submit a 311 request from anywhere (say, Twitter), etc.
These examples only scratch the surface of how apps can “become more civic” (i.e., integrate with government/civic information and services). And that’s only really describing one direction: apps tapping into government information and services.
Another, even more powerful direction is the reverse: helping governments tap into the people-power in web networks. In fact, I heard an amazing stat earlier this year:
It’s incredible to think about how web-enabled networks can extend the reach and increase the leverage of public-interest programs and government services, even when (perhaps especially when) that is not their primary function — i.e., Waze is a traffic avoidance app, not a “civic” app. Other examples include the Airbnb community coming together to provide emergency housing after Sandy, and the Etsy community helping to “craft a comeback” in Rockford, Ill.
In other words, helping all apps “be more civic,” rather than just building more civic apps. I think there is a ton of leverage there, and it’s a direction that has just barely begun to be explored.”