Loomio: “Real democracy is about collaboration: groups of people getting together and making decisions that work for everyone….Loomio is free and open software for anyone, anywhere, to participate in decisions that affect them…
Loomio fills a critical gap: bringing online talk to real world action. Social media and email have made it so easy to communicate, but a decision is what turns talk into action. Right now, there’s no easy way to make decisions together online. It’s like a missing piece of the internet.
We’ve taken all the learning from thousands of groups using our beta prototype and designed a whole new platform for truly inclusive decision-making: Loomio 1.0″
Gordon Crovitz in the Wall Street Journal: “Two new data points for the debate on whether greater access to the Internet leads to more freedom and fewer authoritarian regimes:
According to reports last week, Facebook plans to buy a company that makes solar-powered drones that can hover for years at high altitudes without refueling, which it would use to bring the Internet to parts of the world not yet on the grid. In contrast to this futuristic vision, Russia evoked land grabs of the analog Soviet era by invading Crimea after Ukrainians forced out Vladimir Putin‘s ally as president.
Internet idealists can point to another triumph in helping bring down Ukraine’s authoritarian government. Ukrainian citizens ignored intimidation including officious text messages: “Dear subscriber, you are registered as a participant in a mass disturbance.” Protesters made the most of social media to plan demonstrations and avoid attacks by security forces.
But Mr. Putin quickly delivered the message that social media only goes so far against a fully committed authoritarian. His claim that he had to invade to protect ethnic Russians in Crimea was especially brazen because there had been no loud outcry, on social media or otherwise, among Russian speakers in the region.
A new book reports the state of play on the Internet as a force for freedom. For a decade, Emily Parker, a former Wall Street Journal editorial-page writer and State Department staffer, has researched the role of the Internet in China, Cuba and Russia. The title of her book, “Now I Know Who My Comrades Are,” comes from a blogger in China who explained to Ms. Parker how the Internet helps people discover they are not alone in their views and aspirations for liberty.
Officials in these countries work hard to keep critics isolated and in fear. In Russia, Ms. Parker notes, there is also apathy because the Putin regime seems so entrenched. “Revolutions need a spark, often in the form of a political or economic crisis,” she observes. “Social media alone will not light that spark. What the Internet does create is a new kind of citizen: networked, unafraid, and ready for action.”
Asked about lessons from the invasion of Crimea, Ms. Parker noted that the Internet “chips away at Russia’s control over information.” She added: “Even as Russian state media tries to shape the narrative about Ukraine, ordinary Russians can go online to seek the truth.”
But this same shared awareness may also be accelerating a decline in U.S. influence. In the digital era, U.S. failure to make good on its promises reduces the stature of Washington faster than similar inaction did in the past.
Consider the Hungarian uprising of 1956, the first significant rebellion against Soviet control. The U.S. secretary of state, John Foster Dulles, said: “To all those suffering under communist slavery, let us say you can count on us.” Yet no help came as Soviet tanks rolled into Budapest, tens of thousands were killed, and the leader who tried to secede from the Warsaw Pact, Imre Nagy, was executed.
There were no Facebook posts or YouTube videos instantly showing the result of U.S. fecklessness. In the digital era, scenes of Russian occupation of Crimea are available 24/7. People can watch Mr. Putin’s brazen press conferences and see for themselves what he gets away with.
The U.S. stood by as Syrian civilians were massacred and gassed. There was instant global awareness when President Obama last year backed down from enforcing his “red line” when the Syrian regime used chemical weapons. American inaction in Syria sent a green light for Mr. Putin and others around the world to act with impunity.
Just in recent weeks, Iran tried to ship Syrian rockets to Gaza to attack Israel; Moscow announced it would use bases in Cuba, Venezuela and Nicaragua for its navy and bombers; and China budgeted a double-digit increase in military spending as President Obama cut back the U.S. military.
All institutions are more at risk in this era of instant communication and awareness. Reputations get lost quickly, whether it’s a misstep by a company, a gaffe by a politician, or a lack of resolve by an American president.
Over time, the power of the Internet to bring people together will help undermine authoritarian governments. But as Mr. Putin reminds us, in the short term a peaceful world depends more on a U.S. resolute in using its power and influence to deter aggression.”
Next City: “Boston’s Office of New Urban Mechanics and researchers with Emerson College’s Engagement Game Lab have spent the last few years working to cobble together a methodology for figuring out whether the city’s civic innovations, from apps that track bumpy roads to contests to redesign streetscapes, actually work, and how to fix them when they don’t. They’re out now with a 21-page booklet on what they’ve learned so far, called Design Action Research with Government: A Guidebook.
The DARG approach, as they guidebook’s authors deem it, calls for pairing civic inventors with academics and, through design experimentation and continuous on-the-ground testing, building things that real citizens willingly use.
Take Citizens Connect, the city’s mobile tool for letting the public report problems like potholes, graffiti and broken sidewalks. Launched in 2009 in partnership with the New Hampshire development shop Connected Bits, it has proven popular. But some in the mayor’s office had the sense that users didn’t feel the same connection to the process that someone gets from ringing up the Mayor’s Hotline and explaining to a real-live human about the teenagers bouncing a basketball against a metal garage door at 3am. Speaking with an operator, says Eric Gordon, a professor of civic media who heads Emerson’s Engagement Game Lab, sparks “a certain amount of storytelling and commitment to the issue.”
The Citizens Connect app had been designed with social features, Gordon notes, “but it doesn’t mean that people are going to use it the way you built it.” Indeed, when the researchers started surveying app users, they found that 38 percent never even looked at other users’ complaints.
The DARG methodology, Gordon and his colleagues says, requires them not only to define a goal, but also to think hard about whether it’s a valid ambition. Is it worthwhile to make citizen reporting more social? They decided that it was, because a real objective isn’t just better pothole patching but, says Chris Osgood, co-chair of Boston’s New Urban Mechanics, making good on this bit of wisdom from Jane Jacobs: “Cities have the capability of providing something for everybody, only because, and only when, they are created by everybody.” Gordon and Osgood decided that getting there meant giving citizen reporters a better sense of how they shape day-to-day life in the city.
Their research prompted them to start building a “civic badging” API, or chunk of behind-the-scenes code, called StreetCred. The code can plug into reporting platforms and integrate with existing platforms like Foursquare and Instagram. It can also participate in ‘campaigns’ of activities, like reporting a hundred potholes, checking in at community meetings, and participating in spring clean-up drives. A new version is due out this spring, Gordon says, and eventually outside groups will be able to create their own campaigns through the tool…That guidebook is available here and below.”
New Social media platform called “State”: The simplest way to get your opinions heard.Just state about whatever matters to you, get counted and instantly see where you stand. When everyone’s opinion counts, the full picture emerges. This could make good things happen…
We set up State, because at the moment, most people never get heard. So we’re levelling the playing field for everyone by allowing them to express their opinions quickly and delivering them to the people who most need to hear them.
State lets people communicate in a lucid, non-competitive way. It’s a place where you don’t need hashtags, followers, or fame, just an opinion. The solution we lit upon was at the convergence of design simplicity and semantic intelligence. It allows people to express opinions in a quick and fun way that also provides enough information to interpret, count, and connect them.
For those in positions of leadership or influence, State offers the first many-to-one capability that can precisely map the prevailing sentiment on key issues. These are opinions shared spontaneously, not extracted from a survey.
We believe that everyone deserves a powerful voice online, no one should be left out, and when everyone’s opinions count, a more complete picture emerges. We firmly believe that this could make good things happen.
Paper by Geoff Mulgan in Philosophy & Technology :” Collective intelligence is much talked about but remains very underdeveloped as a field. There are small pockets in computer science and psychology and fragments in other fields, ranging from economics to biology. New networks and social media also provide a rich source of emerging evidence. However, there are surprisingly few useable theories, and many of the fashionable claims have not stood up to scrutiny. The field of analysis should be how intelligence is organised at large scale—in organisations, cities, nations and networks. The paper sets out some of the potential theoretical building blocks, suggests an experimental and research agenda, shows how it could be analysed within an organisation or business sector and points to the possible intellectual barriers to progress.”
McKinsey: “From the invention of the printing press to the telephone, the radio, and the Internet, the ways people collaborate change frequently, and the effects of those changes often reverberate through generations. In this video interview, Clay Shirky, author, New York University professor, and leading thinker on the impact of social media, explains the disruptive impact of technology on how people live and work—and on the economics of what we make and consume. This interview was conducted by McKinsey Global Institute partner Michael Chui, and an edited transcript of Shirky’s remarks follows….
Shirky:…The thing I’ve always looked at, because it is long-term disruptive, is changes in the way people collaborate. Because in the history of particularly the Western world, when communications tools come along and they change how people can contact each other, how they can share information, how they can find each other—we’re talking about the printing press, or the telephone, or the radio, or what have you—the changes that are left in the wake of those new technologies often span generations.
The printing press was a sustaining technology for the scientific revolution, the spread of newspapers, the spread of democracy, just on down the list. So the thing I always watch out for, when any source of disruption comes along, when anything that’s going to upset the old order comes along, is I look for what the collaborative penumbra is.”
New volume of Public Administration and Information Technology series: “Given this global context, and taking into account both the need of academicians and practitioners, it is the intention of this book to shed light on the open government concept and, in particular:
• To provide comprehensive knowledge of recent major developments of open government around the world.
• To analyze the importance of open government efforts for public governance.
• To provide insightful analysis about those factors that are critical when designing, implementing and evaluating open government initiatives.
• To discuss how contextual factors affect open government initiatives’success or failure.
• To explore the existence of theoretical models of open government.
• To propose strategies to move forward and to address future challenges in an international context.”
Russell Brandom at the Verge: “For students of international conflict, 2013 provided plenty to examine. There was civil war in Syria, ethnic violence in China, and riots to the point of revolution in Ukraine. For those working at Duke University’s Ward Lab, all specialists in predicting conflict, the year looks like a betting sheet, full of predictions that worked and others that didn’t pan out.
Guerrilla campaigns intensified, proving out the prediction
When the lab put out their semiannual predictions in July, they gave Paraguay a 97 percent chance of insurgency, largely based on reports of Marxist rebels. The next month, guerrilla campaigns intensified, proving out the prediction. In the case of China’s armed clashes between Uighurs and Hans, the models showed a 33 percent chance of violence, even as the cause of each individual flare-up was concealed by the country’s state-run media. On the other hand, the unrest in Ukraine didn’t start raising alarms until the action had already started, so the country was left off the report entirely.
According to Ward Lab’s staff, the purpose of the project isn’t to make predictions but to test theories. If a certain theory of geopolitics can predict an uprising in Ukraine, then maybe that theory is onto something. And even if these specialists could predict every conflict, it would only be half the battle. “It’s a success only if it doesn’t come at the cost of predicting a lot of incidents that don’t occur,” says Michael D. Ward, the lab’s founder and chief investigator, who also runs the blog Predictive Heuristics. “But it suggests that we might be on the right track.”
If a certain theory of geopolitics can predict an uprising in Ukraine, maybe that theory is onto something
Forecasting the future of a country wasn’t always done this way. Traditionally, predicting revolution or war has been a secretive project, for the simple reason that any reliable prediction would be too valuable to share. But as predictions lean more on data, they’ve actually become harder to keep secret, ushering in a new generation of open-source prediction models that butt against the siloed status quo.
Will this country’s government face an acute existential threat in the next six months?
The story of automated conflict prediction starts at the Defense Advance Research Projects Agency, known as the Pentagon’s R&D wing. In the 1990s, DARPA wanted to try out software-based approaches to anticipating which governments might collapse in the near future. The CIA was already on the case, with section chiefs from every region filing regular forecasts, but DARPA wanted to see if a computerized approach could do better. They looked at a simple question: will this country’s government face an acute existential threat in the next six months? When CIA analysts were put to the test, they averaged roughly 60 percent accuracy, so DARPA’s new system set the bar at 80 percent, looking at 29 different countries in Asia with populations over half a million. It was dubbed ICEWS, the Integrated Conflict Early Warning System, and it succeeded almost immediately, clearing 80 percent with algorithms built on simple regression analysis….
On the data side, researchers at Georgetown University are cataloging every significant political event of the past century into a single database called GDELT, and leaving the whole thing open for public research. Already, projects have used it to map the Syrian civil war and diplomatic gestures between Japan and South Korea, looking at dynamics that had never been mapped before. And then, of course, there’s Ward Lab, releasing a new sheet of predictions every six months and tweaking its algorithms with every development. It’s a mirror of the same open-vs.-closed debate in software — only now, instead of fighting over source code and security audits, it’s a fight over who can see the future the best.”
Mushon Zer-Aviv at School of Data: “Seeing is believing. When working with raw data we’re often encouraged to present it differently, to give it a form, to map it or visualize it. But all maps lie. In fact, maps have to lie, otherwise they wouldn’t be useful. Some are transparent and obvious lies, such as a tree icon on a map often represents more than one tree. Others are white lies – rounding numbers and prioritising details to create a more legible representation. And then there’s the third type of lie, those lies that convey a bias, be it deliberately or subconsciously. A bias that misrepresents the data and skews it towards a certain reading.
It all sounds very sinister, and indeed sometimes it is. It’s hard to see through a lie unless you stare it right in the face, and what better way to do that than to get our minds dirty and look at some examples of creative and mischievous visual manipulation.
Over the past year I’ve had a few opportunities to run Disinformation Visualization workshops, encouraging activists, designers, statisticians, analysts, researchers, technologists and artists to visualize lies. During these sessions I have used the DIKW pyramid (Data > Information > Knowledge > Wisdom), a framework for thinking about how data gains context and meaning and becomes information. This information needs to be consumed and understood to become knowledge. And finally when knowledge influences our insights and our decision making about the future it becomes wisdom. Data visualization is one of the ways to push data up the pyramid towards wisdom in order to affect our actions and decisions. It would be wise then to look at visualizations suspiciously.
Centuries before big data, computer graphics and social media collided and gave us the datavis explosion, visualization was mostly a scientific tool for inquiry and documentation. This history gave the artform its authority as an integral part of the scientific process. Being a product of human brains and hands, a certain degree of bias was always there, no matter how scientific the process was. The effect of these early off-white lies are still felt today, as even our most celebrated interactive maps still echo the biases of the Mercator map projection, grounding Europe and North America on the top of the world, over emphasizing their size and perceived importance over the Global South. Our contemporary practices of programmatically data driven visualization hide both the human eyes and hands that produce them behind data sets, algorithms and computer graphics, but the same biases are still there, only they’re harder to decipher…”
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.
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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.
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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.
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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.
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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.
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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.
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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.”
