The map illustrates how ideas build and flow, merge, and then diverge again over time. Diverse paths are taken to arrive at the final ideas in the article. It also shows how metrics we might presume are meaningful—comments on a post, for example—don’t always correlate with actual influence. We felt the approach we were taking to writing the article was different and disruptive. This visualization confirmed that for us, and helped us learn about crowdsourcing ideas, too.”
Introducing the Data Visualization Checklist
Stephanie Evergreen: “This post has been a long time coming. Ann Emery and I knew some time ago that evaluators and social scientists had a thirst for better graphs, a clear understanding of why better graphs were necessary, but they lacked efficient guidance on how, exactly, to make a graph better. Introducing the Data Visualization Checklist.
Download this checklist and refer to it when you are constructing your next data visualization so that what you produce rocks worlds. Use the checklist to gauge the effectiveness of graphs you’ve already made and adjust places where you don’t score full points. Make copies and slip them into your staff mailboxes.
What’s in the Checklist?
We compiled a set of best practices based on extensive research, tested against the practical day-to-day realities of evaluation practice and the pragmatic needs of our stakeholders. This guidance may not apply to other fields. In fact, we pilot-tested the checklist with a dozen data visualists and found that those who were not in a social science field found more areas of disagreement. That’s ok. Their dissemination purposes are different from ours. Their audiences are not our audiences. You, evaluator, will find clear guidelines on how to make the best use of a graph’s text, color, arrangement, and overall design. We also included a data visualization anatomy chart on the last page of the checklist to illustrate key concepts and point out terminology…”
Global democracy and the democratic minimum: Why a procedural account alone is insufficient
Paper by Klaus Dingwerth in the European Journal of International Relations: “In this critical comment on the global democracy debate, I take stock of contemporary proposals for democratizing global governance. In the first part of the article, I show that, empirically, many international institutions are now evaluated in terms of their democratic credentials. At the same time, the notions of democracy that underpin such evaluations are often very formalistic. They focus on granting access to civil society organizations, making policy-relevant documents available online or establishing global parliamentary assemblies to give citizens a voice in the decision-making of international organizations. In the second part, I challenge this focus on formal procedures and argue that a normatively persuasive conception of global democracy would shift our focus to areas such as health, education and subsistence. Contrary to much contemporary thinking about global democracy, I thus defend the view that the institutions we have are sufficiently democratic. What is needed are not better procedures, but investments that help the weaker members of global society to make effective use of the democracy-relevant institutions that exist in contemporary international politics”
Data.gov Turns Five
NextGov: “When government technology leaders first described a public repository for government data sets more than five years ago, the vision wasn’t totally clear.
“I just didn’t understand what they were talking about,” said Marion Royal of the General Services Administration, describing his first introduction to the project. “I was thinking, ‘this is not going to work for a number of reasons.’”
A few minutes later, he was the project’s program director. He caught onto and helped clarify that vision and since then has worked with a small team to help shepherd online and aggregate more than 100,000 data sets compiled and hosted by agencies across federal, state and local governments.
Many Americans still don’t know what Data.gov is, but chances are good they’ve benefited from the site, perhaps from information such as climate or consumer complaint data. Maybe they downloaded the Red Cross’ Hurricane App after Superstorm Sandy or researched their new neighborhood through a real estate website that drew from government information.
Hundreds of companies pull data they find on the site, which has seen 4.5 million unique visitors from 195 countries, according to GSA. Data.gov has proven a key part of President Obama’s open data policies, which aim to make government more efficient and open as well as to stimulate economic activity by providing private companies, organizations and individuals machine-readable ingredients for new apps, digital tools and programs.”
Free Online Lawmaking Platform for Washington, D.C.
OpenGov Foundation: “At-Large Councilmember David Grosso and The OpenGov Foundation today launched the beta version of MadisonDC, a free online lawmaking tool that empowers citizens to engage directly with their elected officials – and the policymaking process itself – by commenting on, proposing changes to, and debating real D.C. Council legislation. Grosso is the first-ever District elected official to give citizens the opportunity to log on and legislate, putting him at the forefront of a nation-wide movement reinventing local legislatures with technology. Three bills are now open for crowdsourcing on MadisonDC: a plan to fully legalize marijuana, a proposal to make zoning laws more friendly to urban farmers, and legislation to create open primary elections….
MadisonDC is the District of Columbia’s version of the freeMadison software that reinvents government for the Internet Age. Madison 1.0 powered the American people’s successful defense of Internet freedom from Congressional threats. It delivered the first crowdsourced bill in the history of the U.S. Congress. And now, the non-partisan, non-profit OpenGov Foundation has released Madison 2.0, empowering you to participate in your government, efficiently access your elected officials, and hold them accountable.”
How Big Data Could Undo Our Civil-Rights Laws
Virginia Eubanks in the American Prospect: “From “reverse redlining” to selling out a pregnant teenager to her parents, the advance of technology could render obsolete our landmark civil-rights and anti-discrimination laws.
Big Data will eradicate extreme world poverty by 2028, according to Bono, front man for the band U2. But it also allows unscrupulous marketers and financial institutions to prey on the poor. Big Data, collected from the neonatal monitors of premature babies, can detect subtle warning signs of infection, allowing doctors to intervene earlier and save lives. But it can also help a big-box store identify a pregnant teenager—and carelessly inform her parents by sending coupons for baby items to her home. News-mining algorithms might have been able to predict the Arab Spring. But Big Data was certainly used to spy on American Muslims when the New York City Police Department collected license plate numbers of cars parked near mosques, and aimed surveillance cameras at Arab-American community and religious institutions.
Until recently, debate about the role of metadata and algorithms in American politics focused narrowly on consumer privacy protections and Edward Snowden’s revelations about the National Security Agency (NSA). That Big Data might have disproportionate impacts on the poor, women, or racial and religious minorities was rarely raised. But, as Wade Henderson, president and CEO of the Leadership Conference on Civil and Human Rights, and Rashad Robinson, executive director of ColorOfChange, a civil rights organization that seeks to empower black Americans and their allies, point out in a commentary at TPM Cafe, while big data can change business and government for the better, “it is also supercharging the potential for discrimination.”
In his January 17 speech on signals intelligence, President Barack Obama acknowledged as much, seeking to strike a balance between defending “legitimate” intelligence gathering on American citizens and admitting that our country has a history of spying on dissidents and activists, including, famously, Dr. Martin Luther King, Jr. If this balance seems precarious, it’s because the links between historical surveillance of social movements and today’s uses of Big Data are not lost on the new generation of activists.
“Surveillance, big data and privacy have a historical legacy,” says Amalia Deloney, policy director at the Center for Media Justice, an Oakland-based organization dedicated to strengthening the communication effectiveness of grassroots racial justice groups. “In the early 1960s, in-depth, comprehensive, orchestrated, purposeful spying was used to disrupt political movements in communities of color—the Yellow Peril, the American Indian Movement, the Brown Berets, or the Black Panthers—to create fear and chaos, and to spread bias and stereotypes.”
In the era of Big Data, the danger of reviving that legacy is real, especially as metadata collection renders legal protection of civil rights and liberties less enforceable….
Big Data and surveillance are unevenly distributed. In response, a coalition of 14 progressive organizations, including the ACLU, ColorOfChange, the Leadership Conference on Civil and Human Rights, the NAACP, National Council of La Raza, and the NOW Foundation, recently released five “Civil Rights Principles for the Era of Big Data.” In their statement, they demand:
- An end to high-tech profiling;
- Fairness in automated decisions;
- The preservation of constitutional principles;
- Individual control of personal information; and
- Protection of people from inaccurate data.
This historic coalition aims to start a national conversation about the role of big data in social and political inequality. “We’re beginning to ask the right questions,” says O’Neill. “It’s not just about what can we do with this data. How are communities of color impacted? How are women within those communities impacted? We need to fold these concerns into the national conversation.”
Open Data at Core of New Governance Paradigm
GovExec: “Rarely are federal agencies compared favorably with Facebook, Instagram, or other modern models of innovation, but there is every reason to believe they can harness innovation to improve mission effectiveness. After all, Aneesh Chopra, former U.S. Chief Technology Officer, reminded the Excellence in Government 2014 audience that government has a long history of innovation. From nuclear fusion to the Internet, the federal government has been at the forefront of technological development.
According to Chopra, the key to fueling innovation and economic prosperity today is open data. But to make the most of open data, government needs to adapt its culture. Chopra outlined three essential elements of doing so:
- Involve external experts – integrating outside ideas is second to none as a source of innovation.
- Leverage the experience of those on the front lines – federal employees who directly execute their agency’s mission often have the best sense of what does and does not work, and what can be done to improve effectiveness.
- Look to the public as a value multiplier – just as Facebook provides a platform for tens of thousands of developers to provide greater value, federal agencies can provide the raw material for many more to generate better citizen services.
In addition to these three broad elements, Chopra offered four specific levers government can use to help enact this paradigm shift:
- Democratize government data – opening government data to the public facilitates innovation. For example, data provided by the National Oceanic and Atmospheric Administration helps generate a 5 billion dollar industry by maintaining almost no intellectual property constraints on its weather data.
- Collaborate on technical standards – government can act as a convener of industry members to standardize technological development, and thereby increase the value of data shared.
- Issue challenges and prizes – incentivizing the public to get involved and participate in efforts to create value from government data enhances the government’s ability to serve the public.
- Launch government startups – programs like the Presidential Innovation Fellows initiative helps challenge rigid bureaucratic structures and permeate a culture of innovation.
Federal leaders will need a strong political platform to sustain this shift. Fortunately, this blueprint is also bipartisan, says Chopra. Political leaders on both sides of the aisle are already getting behind the movement to bring innovation to the core of government..
Three projects meet the European Job Challenge and receive the Social Innovation Prize
EU Press Release: “Social innovation can be a tool to create new or better jobs, while giving an answer to pressing challenges faced by Europe. Today, Michel Barnier, European Commissioner, has awarded three European Social Innovation prizes to ground-breaking ideas to create new types of work and address social needs. The winning projects aim to help disadvantaged women by employing them to create affordable and limited fashion collections, create jobs in the sector of urban farming, and convert abandoned social housing into learning spaces and entrepreneurship labs.
After the success of the first edition in 2013, the European Commission launched a second round of the Social Innovation Competition in memory of Diogo Vasconcelos1. Its main goal was to invite Europeans to propose new solutions to answer The Job Challenge. The Commission received 1,254 ideas out of which three were awarded with a prize of €30,000 each.
Commissioner Michel Barnier said: “We believe that the winning projects can take advantage of unmet social needs and create sustainable jobs. I want these projects to be scaled up and replicated and inspire more social innovations in Europe. We need to tap into this potential to bring innovative solutions to the needs of our citizens and create new types of work.”
More informationon the Competition page
More jobs for Europe – three outstanding ideas
The following new and exceptional ideas are the winners of the second edition of the European Social Innovation Competition:
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‘From waste to wow! QUID project’ (Italy): fashion business demands perfection, and slightly damaged textile cannot be used for top brands. The project intends to recycle this first quality waste into limited collections and thereby provide jobs to disadvantaged women. This is about creating highly marketable products and social value through recycling.
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‘Urban Farm Lease’ (Belgium): urban agriculture could provide 6,000 direct jobs in Brussels, and an additional 1,500 jobs considering indirect employment (distribution, waste management, training or events). The project aims at providing training, connection and consultancy so that unemployed people take advantage of the large surfaces available for agriculture in the city (e.g. 908 hectares of land or 394 hectares of suitable flat roofs).
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‘Voidstarter’ (Ireland): all major cities in Europe have “voids”, units of social housing which are empty because city councils have insufficient budgets to make them into viable homes. At the same time these cities also experience pressure with social housing provision and homelessness. Voidstarter will provide unemployed people with learning opportunities alongside skilled tradespersons in the refurbishing of the voids.”
The Secret Science of Retweets
Emerging Technology From the arXiv: “If you send a tweet to a stranger asking them to retweet it, you probably wouldn’t be surprised if they ignored you entirely. But if you sent out lots of tweets like this, perhaps a few might end up being passed on.
How come? What makes somebody retweet information from a stranger? That’s the question addressed today by Kyumin Lee from Utah State University in Logan and a few pals from IBM’s Almaden research center in San Jose….by studying the characteristics of Twitter users, it is possible to identify strangers who are more likely to pass on your message than others. And in doing this, the researchers say they’ve been able to improve the retweet rate of messages sent strangers by up to 680 percent.
So how did they do it? The new technique is based on the idea that some people are more likely to tweet than others, particularly on certain topics and at certain times of the day. So the trick is to find these individuals and target them when they are likely to be most effective.
So the approach was straightforward. The idea is to study the individuals on Twitter, looking at their profiles and their past tweeting behavior, looking for clues that they might be more likely to retweet certain types of information. Having found these individuals, send your tweets to them.
That’s the theory. In practice, it’s a little more involved. Lee and co wanted to test people’s response to two types of information: local news (in San Francisco) and tweets about bird flu, a significant issue at the time of their research. They then created several Twitter accounts with a few followers, specifically to broadcast information of this kind.
Next, they selected people to receive their tweets. For the local news broadcasts, they searched for Twitter users geolocated in the Bay area, finding over 34,000 of them and choosing 1,900 at random.
They then a sent a single message to each user of the format:
“@ SFtargetuser “A man was killed and three others were wounded in a shooting … http://bit.ly/KOl2sC” Plz RT this safety news”
So the tweet included the user’s name, a short headline, a link to the story and a request to retweet.
Of these 1,900 people, 52 retweeted the message they received. That’s 2.8 percent.
For the bird flu information, Lee and co hunted for people who had already tweeted about bird flu, finding 13,000 of them and choosing 1,900 at random. Of these, 155 retweeted the message they received, a retweet rate of 8.4 percent.
But Lee and co found a way to significantly improve these retweet rates. They went back to the original lists of Twitter users and collected publicly available information about each of them, such as their personal profile, the number of followers, the people they followed, their 200 most recent tweets and whether they retweeted the message they had received
Next, the team used a machine learning algorithm to search for correlations in this data that might predict whether somebody was more likely to retweet. For example, they looked at whether people with older accounts were more likely to retweet or how the ratio of friends to followers influenced the retweet likelihood, or even how the types of negative or positive words they used in previous tweets showed any link. They also looked at the time of day that people were most active in tweeting.
The result was a machine learning algorithm capable of picking users who were most likely to retweet on a particular topic.
And the results show that it is surprisingly effective. When the team sent local information tweets to individuals identified by the algorithm, 13.3 percent retweeted it, compared to just 2.6 percent of people chosen at random.
And they got even better results when they timed the request to match the periods when people had been most active in the past. In that case, the retweet rate rose to 19.3 percent. That’s an improvement of over 600 percent.
Similarly, the rate for bird flu information rose from 8.3 percent for users chosen at random to 19.7 percent for users chosen by the algorithm.
That’s a significant result that marketers, politicians, news organizations will be eyeing with envy.
An interesting question is how they can make this technique more generally applicable. It raises the prospect of an app that allows anybody to enter a topic of interest and which then creates a list of people most likely to retweet on that topic in the next few hours.
Lee and co do not mention any plans of this kind. But if they don’t exploit it, then there will surely be others who will.
Ref: arxiv.org/abs/1405.3750 : Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information”
The Collective Intelligence Handbook: an open experiment
Michael Bernstein: “Is there really a wisdom of the crowd? How do we get at it and understand it, utilize it, empower it?
You probably have some ideas about this. I certainly do. But I represent just one perspective. What would an economist say? A biologist? A cognitive or social psychologist? An artificial intelligence or human-computer interaction researcher? A communications scholar?
For the last two years, Tom Malone (MIT Sloan) and I (Stanford CS) have worked to bring together all these perspectives into one book. We are nearing completion, and the Collective Intelligence Handbook will be published by the MIT Press later this year. I’m still relatively dumbfounded by the rockstar lineup we have managed to convince to join up.
It’s live.
Today we went live with the authors’ current drafts of the chapters. All the current preprints are here: http://cci.mit.edu/CIchapterlinks.html
And now is when you come in.
But we’re not done. We’d love for you — the crowd — to help us make this book better. We envisioned this as an open process, and we’re excited that all the chapters are now at a point where we’re ready for critique, feedback, and your contributions.
There are two ways you can help:
- Read the current drafts and leave comments inline in the Google Docs to help us make them better.
- Drop suggestions in the separate recommended reading list for each chapter. We (the editors) will be using that material to help us write an introduction to each chapter.
We have one month. The authors’ final chapters are due to us in mid-June. So off we go!”
Here’s what’s in the book:
Chapter 1. Introduction
Thomas W. Malone (MIT) and Michael S. Bernstein (Stanford University)
What is collective intelligence, anyway?
Chapter 2. Human-Computer Interaction and Collective Intelligence
Jeffrey P. Bigham (Carnegie Mellon University), Michael S. Bernstein (Stanford University), and Eytan Adar (University of Michigan)
How computation can help gather groups of people to tackle tough problems together.
Chapter 3. Artificial Intelligence and Collective Intelligence
Daniel S. Weld (University of Washington), Mausam (IIT Delhi), Christopher H. Lin (University of Washington), and Jonathan Bragg (University of Washington)
Mixing machine intelligence with human intelligence could enable a synthesized intelligent actor that brings together the best of both worlds.
Chapter 4. Collective Behavior in Animals: An Ecological Perspective
Deborah M. Gordon (Stanford University)
How do groups of animals work together in distributed ways to solve difficult problems?
Chapter 5. The Wisdom of Crowds vs. the Madness of Mobs
Andrew W. Lo (MIT)
Economics has studied a collectively intelligent forum — the market — for a long time. But are we as smart as we think we are?
Chapter 6. Collective Intelligence in Teams and Organizations
Anita Williams Woolley (Carnegie Mellon University), Ishani Aggarwal (Georgia Tech), Thomas W. Malone (MIT)
How do the interactions between groups of people impact how intelligently that group acts?
Chapter 7. Cognition and Collective Intelligence
Mark Steyvers (University of California, Irvine), Brent Miller (University of California, Irvine)
Understanding the conditions under which people are smart individually can help us predict when they might be smart collectively.
Chapter 8. Peer Production: A Modality of Collective Intelligence
Yochai Benkler (Harvard University), Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington)
What have collective efforts such as Wikipedia taught us about how large groups come together to create knowledge and creative artifacts?