Springwise: “Most people are uneasy about companies making money from the personal data they make available online, but are happy to turn a blind eye if it means they can continue using services such as Facebook for free. Aiming to give web users more control over what they share, Datacoup is a marketplace that lets anyone sell their personal information direct to advertisers.
The data we create on platforms such as Facebook, Twitter, Amazon and Google are worth billions of dollars to advertisers, data brokers and businesses. Through Datacoup, users pick and choose basic information, real time social feeds and even credit and debit card purchases if they’re happy to share them with advertisers, as well deciding which brands can buy their information. Datacoup stores the data — which is all anonymous — under bank-level encryption and acts as a broker to sell it to businesses who want it. It then hands a portion of the sale — typically around USD 8 — back to users on a monthly basis…”
The disruptive power of collaboration: An interview with Clay Shirky
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.”
Open Government -Opportunities and Challenges for Public Governance
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.”
What is (and what is not) open government
“This Prezi by César Nicandro Cruz-Rubiois designed for educational purposes. It presents open government concept and uses some Youtube source videos in order to give some examples”
Disinformation Visualization: How to lie with datavis
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…”
Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters
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 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 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: 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: 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 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 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.”
Can We Balance Data Protection With Value Creation?
A “privacy perspective” by Sara Degli Esposti: “In the last few years there has been a dramatic change in the opportunities organizations have to generate value from the data they collect about customers or service users. Customers and users are rapidly becoming collections of “data points” and organizations can learn an awful lot from the analysis of this huge accumulation of data points, also known as “Big Data.”
Organizations are perhaps thrilled, dreaming about new potential applications of digital data but also a bit concerned about hidden risks and unintended consequences. Take, for example, the human rights protections placed on personal data by the EU. Regulators are watching closely, intending to preserve the eight basic privacy principles without compromising the free flow of information.
Some may ask whether it’s even possible to balance the two.
Enter the Big Data Protection Project (BDPP): an Open University study on organizations’ ability to leverage Big Data while complying with EU data protection principles. The study represents a chance for you to contribute to, and learn about, the debate on the reform of the EU Data Protection Directive. It is open to staff with interests in data management or use, from all types of organizations, both for-profit and nonprofit, with interests in Europe.
Join us by visiting the study’s page on the Open University website. Participants will receive a report with all the results. The BDP is a scientific project—no commercial organization is involved—with implications relevant to both policy-makers and industry representatives..
What kind of legislation do we need to create that positive system of incentive for organizations to innovate in the privacy field?
There is no easy answer.
That’s why we need to undertake empirical research into actual information management practices to understand the effects of regulation on people and organizations. Legal instruments conceived with the best intentions can be ineffective or detrimental in practice. However, other factors can also intervene and motivate business players to develop procedures and solutions which go far beyond compliance. Good legislation should complement market forces in bringing values and welfare to both consumers and organizations.
Is European data protection law keeping its promise of protecting users’ information privacy while contributing to the flourishing of the digital economy or not? Will the proposed General Data Protection Regulation (GDPR) be able to achieve this goal? What would you suggest to do to motivate organizations to invest in information security and take information privacy seriously?
Let’s consider for a second some basic ideas such as the eight fundamental data protection principles: notice, consent, purpose specification and limitation, data quality, respect of data subjects’ rights, information security and accountability. Many of these ideas are present in the EU 1995 Data Protection Directive, the U.S. Fair Information Practice Principles (FIPPs) andthe 1980 OECD Guidelines. The fundamental question now is, should all these ideas be brought into the future, as suggested in the proposed new GDPR, orshould we reconsider our approach and revise some of them, as recommended in the 21st century version of the 1980 OECD Guidelines?
As you may know, notice and consent are often taken as examples of how very good intentions can be transformed into actions of limited importance. Rather than increase people’s awareness of the growing data economy, notice and consent have produced a tick-box tendency accompanied by long and unintelligible privacy policies. Besides, consent is rarely freely granted. Individuals give their consent in exchange for some product or service or as part of a job relationship. The imbalance between the two goods traded—think about how youngsters perceive not having access to some social media as a form of social exclusion—and the lack of feasible alternatives often make an instrument, such as the current use made of consent, meaningless.
On the other hand, a principle such as data quality, which has received very limited attention, could offer opportunities to policy-makers and businesses to reopen the debate on users’ control of their personal data. Having updated, accurate data is something very valuable for organizations. Data quality is also key to the success of many business models. New partnerships between users and organizations could be envisioned under this principle.
Finally, data collection limitation and purpose specification could be other examples of the divide between theory and practice: The tendency we see is that people and businesses want to share, merge and reuse data over time and to do new and unexpected things. Of course, we all want to avoid function creep and prevent any detrimental use of our personal data. We probably need new, stronger mechanisms to ensure data are used for good purposes.
Digital data have become economic assets these days. We need good legislation to stop the black market for personal data and open the debate on how each of us wants to contribute to, and benefit from, the data economy.”
Some may ask whether it’s even possible to balance the two.
Enter the Big Data Protection Project (BDPP): an Open University study on organizations’ ability to leverage Big Data while complying with EU data protection principles. The study represents a chance for you to contribute to, and learn about, the debate on the reform of the EU Data Protection Directive. It is open to staff with interests in data management or use, from all types of organizations, both for-profit and nonprofit, with interests in Europe.
Join us by visiting the study’s page on the Open University website. Participants will receive a report with all the results. The BDP is a scientific project—no commercial organization is involved—with implications relevant to both policy-makers and industry representatives..
What kind of legislation do we need to create that positive system of incentive for organizations to innovate in the privacy field?
There is no easy answer.
That’s why we need to undertake empirical research into actual information management practices to understand the effects of regulation on people and organizations. Legal instruments conceived with the best intentions can be ineffective or detrimental in practice. However, other factors can also intervene and motivate business players to develop procedures and solutions which go far beyond compliance. Good legislation should complement market forces in bringing values and welfare to both consumers and organizations.
Is European data protection law keeping its promise of protecting users’ information privacy while contributing to the flourishing of the digital economy or not? Will the proposed General Data Protection Regulation (GDPR) be able to achieve this goal? What would you suggest to do to motivate organizations to invest in information security and take information privacy seriously?
Let’s consider for a second some basic ideas such as the eight fundamental data protection principles: notice, consent, purpose specification and limitation, data quality, respect of data subjects’ rights, information security and accountability. Many of these ideas are present in the EU 1995 Data Protection Directive, the U.S. Fair Information Practice Principles (FIPPs) andthe 1980 OECD Guidelines. The fundamental question now is, should all these ideas be brought into the future, as suggested in the proposed new GDPR, orshould we reconsider our approach and revise some of them, as recommended in the 21st century version of the 1980 OECD Guidelines?
As you may know, notice and consent are often taken as examples of how very good intentions can be transformed into actions of limited importance. Rather than increase people’s awareness of the growing data economy, notice and consent have produced a tick-box tendency accompanied by long and unintelligible privacy policies. Besides, consent is rarely freely granted. Individuals give their consent in exchange for some product or service or as part of a job relationship. The imbalance between the two goods traded—think about how youngsters perceive not having access to some social media as a form of social exclusion—and the lack of feasible alternatives often make an instrument, such as the current use made of consent, meaningless.
On the other hand, a principle such as data quality, which has received very limited attention, could offer opportunities to policy-makers and businesses to reopen the debate on users’ control of their personal data. Having updated, accurate data is something very valuable for organizations. Data quality is also key to the success of many business models. New partnerships between users and organizations could be envisioned under this principle.
Finally, data collection limitation and purpose specification could be other examples of the divide between theory and practice: The tendency we see is that people and businesses want to share, merge and reuse data over time and to do new and unexpected things. Of course, we all want to avoid function creep and prevent any detrimental use of our personal data. We probably need new, stronger mechanisms to ensure data are used for good purposes.
Digital data have become economic assets these days. We need good legislation to stop the black market for personal data and open the debate on how each of us wants to contribute to, and benefit from, the data economy.”
Selfiecity
New Project aimed at investigating the style of self-portraits (selfies) in five cities across the world: “Selfiecity investigates selfies using a mix of theoretic, artistic and quantitative methods:
- We present our findings about the demographics of people taking selfies, their poses and expressions.
- Rich media visualizations (imageplots) assemble thousands of photos to reveal interesting patterns.
- The interactive selfiexploratory allows you to navigate the whole set of 3200 photos.
- Finally, theoretical essays discuss selfies in the history of photography, the functions of images in social media, and methods and dataset.”
Can Twitter Predict Major Events Such As Mass Protests?
Emerging Technology From the arXiv : “The idea that social media sites such as Twitter can predict the future has a controversial history. In the last few years, various groups have claimed to be able to predict everything from the outcome of elections to the box office takings for new movies.
It’s fair to say that these claims have generated their fair share of criticism. So it’s interesting to see a new claim come to light.
Today, Nathan Kallus at the Massachusetts Institute of Technology in Cambridge says he has developed a way to predict crowd behaviour using statements made on Twitter. In particular, he has analysed the tweets associated with the 2013 coup d’état in Egypt and says that the civil unrest associated with this event was clearly predictable days in advance.
It’s not hard to imagine how the future behaviour of crowds might be embedded in the Twitter stream. People often signal their intent to meet in advance and even coordinate their behaviour using social media. So this social media activity is a leading indicator of future crowd behaviour.
That makes it seem clear that predicting future crowd behaviour is simply a matter of picking this leading indicator out of the noise.
Kallus says this is possible by mining tweets for any mention of future events and then analysing trends associated with them. “The gathering of crowds into a single action can often be seen through trends appearing in this data far in advance,” he says.
It turns out that exactly this kind of analysis is available from a company called Recorded Future based in Cambridge, which scans 300,000 different web sources in seven different languages from all over the world. It then extracts mentions of future events for later analysis….
The bigger question is whether it’s possible to pick out this evidence in advance. In other words, is possible to make predictions before the events actually occur?
That’s not so clear but there are good reasons to be cautious. First of all, while it’s possible to correlate Twitter activity to real protests, it’s also necessary to rule out false positives. There may be significant Twitter trends that do not lead to significant protests in the streets. Kallus does not adequately address the question of how to tell these things apart.
Then there is the question of whether tweets are trustworthy. It’s not hard to imagine that when it comes to issues of great national consequence, propaganda, rumor and irony may play a significant role. So how to deal with this?
There is also the question of demographics and whether tweets truly represent the intentions and activity of the population as a whole. People who tweet are overwhelmingly likely to be young but there is another silent majority that plays hugely important role. So can the Twitter firehose really represent the intentions of this part of the population too?
The final challenge is in the nature of prediction. If the Twitter feed is predictive, then what’s needed is evidence that it can be used to make real predictions about the future and not just historical predictions about the past.
We’ve looked at some of these problems with the predictive power of social media before and the challenge is clear: if there is a claim to be able to predict the future, then this claim must be accompanied by convincing evidence of an actual prediction about an event before it happens.
Until then, it would surely be wise to be circumspect about the predictive powers of Twitter and other forms of social media.
Ref: arxiv.org/abs/1402.2308: Predicting Crowd Behavior with Big Public Data”
Crowdsourcing and regulatory reviews: A new way of challenging red tape in British government?
New paper by Martin Lodge and Kai Wegrich in Regulation and Governance: “Much has been said about the appeal of digital government devices to enhance consultation on rulemaking. This paper explores the most ambitious attempt by the UK central government so far to draw on “crowdsourcing” to consult and act on regulatory reform, the “Red Tape Challenge.” We find that the results of this exercise do not represent any major change to traditional challenges to consultation processes. Instead, we suggest that the extensive institutional arrangements for crowdsourcing were hardly significant in informing actual policy responses: neither the tone of the crowdsourced comments, the direction of the majority views, nor specific comments were seen to matter. Instead, it was processes within the executive that shaped the overall governmental responses to this initiative. The findings, therefore, provoke wider debates about the use of social media in rulemaking and consultation exercises.”