Is Social Media Good or Bad for Democracy?

Curated on Posted on by Stefaan Verhulst

Essay by Cass R. Sunstein,  as  part of a series by Facebook on social media and democracy: “On balance, the question of whether social media platforms are good for democracy is easy. On balance, they are not merely good; they are terrific. For people to govern themselves, they need to have information. They also need to be able to convey it to others. Social media platforms make that tons easier.

There is a subtler point as well. When democracies are functioning properly, people’s sufferings and challenges are not entirely private matters. Social media platforms help us alert one another to a million and one different problems. In the process, the existence of social media can prod citizens to seek solutions.

Consider the remarkable finding, by the economist Amartya Sen, that in the history of the world, there has never been a famine in a system with a democratic press and free elections. A central reason is that famines are a product not only of a scarcity of food, but also a nation’s failure to provide solutions. When the press is free, and when leaders are elected, leaders have a strong incentive to help.

Mental illness, chronic pain, loss of employment, vulnerability to crime, drugs in the family – information about all these spread via social media, and they can be reduced with sensible policies. When people can talk to each other, and disclose what they know to public officials, the whole world might change in a hurry.

But celebrations can be awfully boring, so let’s hold the applause. Are automobiles good for transportation? Absolutely, but in the United States alone, over 35,000 people died in crashes in 2016.

Social media platforms are terrific for democracy in many ways, but pretty bad in others. And they remain a work-in-progress, not only because of new entrants, but also because the not-so-new ones (including Facebook) continue to evolve. What John Dewey said about my beloved country is true for social media as well: “The United States are not yet made; they are not a finished fact to be categorically assessed.”

For social media and democracy, the equivalents of car crashes include false reports (“fake news”) and the proliferation of information cocoons — and as a result, an increase in fragmentation, polarization and extremism. If you live in an information cocoon, you will believe many things that are false, and you will fail to learn countless things that are true. That’s awful for democracy. And as we have seen, those with specific interests — including politicians and nations, such as Russia, seeking to disrupt democratic processes — can use social media to promote those interests.

This problem is linked to the phenomenon of group polarization — which takes hold when like-minded people talk to one another and end up thinking a more extreme version of what they thought before they started to talk. In fact that’s a common outcome. At best, it’s a problem. At worst, it’s dangerous….(More)”.

How the Data That Internet Companies Collect Can Be Used for the Public Good

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst and Andrew Young at Harvard Business Review: “…In particular, the vast streams of data generated through social media platforms, when analyzed responsibly, can offer insights into societal patterns and behaviors. These types of behaviors are hard to generate with existing social science methods. All this information poses its own problems, of complexity and noise, of risks to privacy and security, but it also represents tremendous potential for mobilizing new forms of intelligence.

In a recent report, we examine ways to harness this potential while limiting and addressing the challenges. Developed in collaboration with Facebook, the report seeks to understand how public and private organizations can join forces to use social media data — through data collaboratives — to mitigate and perhaps solve some our most intractable policy dilemmas.

Data Collaboratives: Public-Private Partnerships for Our Data Age 

For all of data’s potential to address public challenges, most data generated today is collected by the private sector. Typically ensconced in corporate databases, and tightly held in order to maintain competitive advantage, this data contains tremendous possible insights and avenues for policy innovation. But because the analytical expertise brought to bear on it is narrow, and limited by private ownership and access restrictions, its vast potential often goes untapped.

Data collaboratives offer a way around this limitation. They represent an emerging public-private partnership model, in which participants from different areas , including the private sector, government, and civil society , can come together to exchange data and pool analytical expertise in order to create new public value. While still an emerging practice, examples of such partnerships now exist around the world, across sectors and public policy domains….

Professionalizing the Responsible Use of Private Data for Public Good

For all its promise, the practice of data collaboratives remains ad hoc and limited. In part, this is a result of the lack of a well-defined, professionalized concept of data stewardship within corporations. Today, each attempt to establish a cross-sector partnership built on the analysis of social media data requires significant and time-consuming efforts, and businesses rarely have personnel tasked with undertaking such efforts and making relevant decisions.

As a consequence, the process of establishing data collaboratives and leveraging privately held data for evidence-based policy making and service delivery is onerous, generally one-off, not informed by best practices or any shared knowledge base, and prone to dissolution when the champions involved move on to other functions.

By establishing data stewardship as a corporate function, recognized within corporations as a valued responsibility, and by creating the methods and tools needed for responsible data-sharing, the practice of data collaboratives can become regularized, predictable, and de-risked.

If early efforts toward this end — from initiatives such as Facebook’s Data for Good efforts in the social media space and MasterCard’s Data Philanthropy approach around finance data — are meaningfully scaled and expanded, data stewards across the private sector can act as change agents responsible for determining what data to share and when, how to protect data, and how to act on insights gathered from the data.

Still, many companies (and others) continue to balk at the prospect of sharing “their” data, which is an understandable response given the reflex to guard corporate interests. But our research has indicated that many benefits can accrue not only to data recipients but also to those who share it. Data collaboration is not a zero-sum game.

With support from the Hewlett Foundation, we are embarking on a two-year project toward professionalizing data stewardship (and the use of data collaboratives) and establishing well-defined data responsibility approaches. We invite others to join us in working to transform this practice into a widespread, impactful means of leveraging private-sector assets, including social media data, to create positive public-sector outcomes around the world….(More)”.

 

Open Data Risk Assessment

Curated on Posted on by Stefaan Verhulst

Report by the Future of Privacy Forum: “The transparency goals of the open data movement serve important social, economic, and democratic functions in cities like Seattle. At the same time, some municipal datasets about the city and its citizens’ activities carry inherent risks to individual privacy when shared publicly. In 2016, the City of Seattle declared in its Open Data Policy that the city’s data would be “open by preference,” except when doing so may affect individual privacy. To ensure its Open Data Program effectively protects individuals, Seattle committed to performing an annual risk assessment and tasked the Future of Privacy Forum (FPF) with creating and deploying an initial privacy risk assessment methodology for open data.

This Report provides tools and guidance to the City of Seattle and other municipalities navigating the complex policy, operational, technical, organizational, and ethical standards that support privacyprotective open data programs. Although there is a growing body of research regarding open data privacy, open data managers and departmental data owners need to be able to employ a standardized methodology for assessing the privacy risks and benefits of particular datasets internally, without access to a bevy of expert statisticians, privacy lawyers, or philosophers. By optimizing its internal processes and procedures, developing and investing in advanced statistical disclosure control strategies, and following a flexible, risk-based assessment process, the City of Seattle – and other municipalities – can build mature open data programs that maximize the utility and openness of civic data while minimizing privacy risks to individuals and addressing community concerns about ethical challenges, fairness, and equity.

This Report first describes inherent privacy risks in an open data landscape, with an emphasis on potential harms related to re-identification, data quality, and fairness. To address these risks, the Report includes a Model Open Data Benefit-Risk Analysis (“Model Analysis”). The Model Analysis evaluates the types of data contained in a proposed open dataset, the potential benefits – and concomitant risks – of releasing the dataset publicly, and strategies for effective de-identification and risk mitigation. This holistic assessment guides city officials to determine whether to release the dataset openly, in a limited access environment, or to withhold it from publication (absent countervailing public policy considerations). …(More)”.

After Big Data: The Coming Age of “Big Indicators”

Curated on Posted on by Stefaan Verhulst

Andrew Zolli at the Stanford Social Innovation Review: “Consider, for a moment, some of the most pernicious challenges facing humanity today: the increasing prevalence of natural disasters; the systemic overfishing of the world’s oceans; the clear-cutting of primeval forests; the maddening persistence of poverty; and above all, the accelerating effects of global climate change.

Each item in this dark litany inflicts suffering on the world in its own, awful way. Yet as a group, they share some common characteristics. Each problem is messy, with lots of moving parts. Each is riddled with perverse incentives, which can lead local actors to behave in a way that is not in the common interest. Each is opaque, with dynamics that are only partially understood, even by experts; each can, as a result, often be made worse by seemingly rational and well-intentioned interventions. When things do go wrong, each has consequences that diverge dramatically from our day-to-day experiences, making their full effects hard to imagine, predict, and rehearse. And each is global in scale, raising questions about who has the legal obligation to act—and creating incentives for leaders to disavow responsibility (and sometimes even question the legitimacy of the problem itself).

With dynamics like these, it’s little wonder systems theorists label these kinds of problems “wicked” or even “super wicked.” It’s even less surprising that these challenges remain, by and large, externalities to the global system—inadequately measured, perennially underinvested in, and poorly accounted for—until their consequences spill disastrously and expensively into view.

For real progress to occur, we’ve got to move these externalities into the global system, so that we can fully assess their costs, and so that we can sufficiently incentivize and reward stakeholders for addressing them and penalize them if they don’t. And that’s going to require a revolution in measurement, reporting, and financial instrumentation—the mechanisms by which we connect global problems with the resources required to address them at scale.

Thankfully, just such a revolution is under way.

It’s a complex story with several moving parts, but it begins with important new technical developments in three critical areas of technology: remote sensing and big data, artificial intelligence, and cloud computing.

Remote sensing and big data allow us to collect unprecedented streams of observations about our planet and our impacts upon it, and dramatic advances in AI enable us to extract the deeper meaning and patterns contained in those vast data streams. The rise of the cloud empowers anyone with an Internet connection to access and interact with these insights, at a fraction of the traditional cost.

In the years to come, these technologies will shift much of the current conversation focused on big data to one focused on “big indicators”—highly detailed, continuously produced, global indicators that track change in the health of the Earth’s most important systems, in real time. Big indicators will form an important mechanism for guiding human action, allow us to track the impact of our collective actions and interventions as never before, enable better and more timely decisions, transform reporting, and empower new kinds of policy and financing instruments. In short, they will reshape how we tackle a number of global problems, and everyone—especially nonprofits, NGOs, and actors within the social and environmental sectors—will play a role in shaping and using them….(More)”.

Improving refugee integration through data-driven algorithmic assignment

Curated on Posted on by Stefaan Verhulst

Kirk Bansak, et al in Science Magazine: “Developed democracies are settling an increased number of refugees, many of whom face challenges integrating into host societies. We developed a flexible data-driven algorithm that assigns refugees across resettlement locations to improve integration outcomes. The algorithm uses a combination of supervised machine learning and optimal matching to discover and leverage synergies between refugee characteristics and resettlement sites.

The algorithm was tested on historical registry data from two countries with different assignment regimes and refugee populations, the United States and Switzerland. Our approach led to gains of roughly 40 to 70%, on average, in refugees’ employment outcomes relative to current assignment practices. This approach can provide governments with a practical and cost-efficient policy tool that can be immediately implemented within existing institutional structures….(More)”.

Urban Big Data: City Management and Real Estate Markets

Curated on Posted on by Stefaan Verhulst

Report by Richard Barkham, Sheharyar Bokhari and Albert Saiz: “In this report, we discuss recent trends in the application of urban big data and their impact on real estate markets. We expect such technologies to improve quality of life and the productivity of cities over the long run.

We forecast that smart city technologies will reinforce the primacy of the most successful global metropolises at least for a decade or more. A few select metropolises in emerging countries may also leverage these technologies to leapfrog on the provision of local public services.

In the long run, all cities throughout the urban system will end up adopting successful and cost-effective smart city initiatives. Nevertheless, smaller-scale interventions are likely to crop up everywhere, even in the short run. Such targeted programs are more likely to improve conditions in blighted or relatively deprived neighborhoods, which could generate gentrification and higher valuations there. It is unclear whether urban information systems will have a centralizing or suburbanizing impact. They are likely to make denser urban centers more attractive, but they are also bound to make suburban or exurban locations more accessible…(More)”.

They Are Watching You—and Everything Else on the Planet

Curated on Posted on by Stefaan Verhulst

Cover article by Robert Draper for Special Issue of the National Geographic: “Technology and our increasing demand for security have put us all under surveillance. Is privacy becoming just a memory?…

In 1949, amid the specter of European authoritarianism, the British novelist George Orwell published his dystopian masterpiece 1984, with its grim admonition: “Big Brother is watching you.” As unsettling as this notion may have been, “watching” was a quaintly circumscribed undertaking back then. That very year, 1949, an American company released the first commercially available CCTV system. Two years later, in 1951, Kodak introduced its Brownie portable movie camera to an awestruck public.

Today more than 2.5 trillion images are shared or stored on the Internet annually—to say nothing of the billions more photographs and videos people keep to themselves. By 2020, one telecommunications company estimates, 6.1 billion people will have phones with picture-taking capabilities. Meanwhile, in a single year an estimated 106 million new surveillance cameras are sold. More than three million ATMs around the planet stare back at their customers. Tens of thousands of cameras known as automatic number plate recognition devices, or ANPRs, hover over roadways—to catch speeding motorists or parking violators but also, in the case of the United Kingdom, to track the comings and goings of suspected criminals. The untallied but growing number of people wearing body cameras now includes not just police but also hospital workers and others who aren’t law enforcement officers. Proliferating as well are personal monitoring devices—dash cams, cyclist helmet cameras to record collisions, doorbells equipped with lenses to catch package thieves—that are fast becoming a part of many a city dweller’s everyday arsenal. Even less quantifiable, but far more vexing, are the billions of images of unsuspecting citizens captured by facial-recognition technology and stored in law enforcement and private-sector databases over which our control is practically nonexistent.

Those are merely the “watching” devices that we’re capable of seeing. Presently the skies are cluttered with drones—2.5 million of which were purchased in 2016 by American hobbyists and businesses. That figure doesn’t include the fleet of unmanned aerial vehicles used by the U.S. government not only to bomb terrorists in Yemen but also to help stop illegal immigrants entering from Mexico, monitor hurricane flooding in Texas, and catch cattle thieves in North Dakota. Nor does it include the many thousands of airborne spying devices employed by other countries—among them Russia, China, Iran, and North Korea.

We’re being watched from the heavens as well. More than 1,700 satellites monitor our planet. From a distance of about 300 miles, some of them can discern a herd of buffalo or the stages of a forest fire. From outer space, a camera clicks and a detailed image of the block where we work can be acquired by a total stranger….

This is—to lift the title from another British futurist, Aldous Huxley—our brave new world. That we can see it coming is cold comfort since, as Carnegie Mellon University professor of information technology Alessandro Acquisti says, “in the cat-and-mouse game of privacy protection, the data subject is always the weaker side of the game.” Simply submitting to the game is a dispiriting proposition. But to actively seek to protect one’s privacy can be even more demoralizing. University of Texas American studies professor Randolph Lewis writes in his new book, Under Surveillance: Being Watched in Modern America, “Surveillance is often exhausting to those who really feel its undertow: it overwhelms with its constant badgering, its omnipresent mysteries, its endless tabulations of movements, purchases, potentialities.”

The desire for privacy, Acquisti says, “is a universal trait among humans, across cultures and across time. You find evidence of it in ancient Rome, ancient Greece, in the Bible, in the Quran. What’s worrisome is that if all of us at an individual level suffer from the loss of privacy, society as a whole may realize its value only after we’ve lost it for good.”…(More)”.

Extracting crowd intelligence from pervasive and social big data

Curated on Posted on by Stefaan Verhulst

Introduction by Leye Wang, Vincent Gauthier, Guanling Chen and Luis Moreira-Matias of Special Issue of the Journal of Ambient Intelligence and Humanized Computing: “With the prevalence of ubiquitous computing devices (smartphones, wearable devices, etc.) and social network services (Facebook, Twitter, etc.), humans are generating massive digital traces continuously in their daily life. Considering the invaluable crowd intelligence residing in these pervasive and social big data, a spectrum of opportunities is emerging to enable promising smart applications for easing individual life, increasing company profit, as well as facilitating city development. However, the nature of big data also poses fundamental challenges on the techniques and applications relying on the pervasive and social big data from multiple perspectives such as algorithm effectiveness, computation speed, energy efficiency, user privacy, server security, data heterogeneity and system scalability. This special issue presents the state-of-the-art research achievements in addressing these challenges. After the rigorous review process of reviewers and guest editors, eight papers were accepted as follows.

The first paper “Automated recognition of hypertension through overnight continuous HRV monitoring” by Ni et al. proposes a non-invasive way to differentiate hypertension patients from healthy people with the pervasive sensors such as a waist belt. To this end, the authors train a machine learning model based on the heart rate data sensed from waists worn by a crowd of people, and the experiments show that the detection accuracy is around 93%.

The second paper “The workforce analyzer: group discovery among LinkedIn public profiles” by Dai et al. describes two users’ group discovery methods among LinkedIn public profiles. One is based on K-means and another is based on SVM. The authors contrast results of both methods and provide insights about the trending professional orientations of the workforce from an online perspective.

The third paper “Tweet and followee personalized recommendations based on knowledge graphs” by Pla Karidi et al. present an efficient semantic recommendation method that helps users filter the Twitter stream for interesting content. The foundation of this method is a knowledge graph that can represent all user topics of interest as a variety of concepts, objects, events, persons, entities, locations and the relations between them. An important advantage of the authors’ method is that it reduces the effects of problems such as over-recommendation and over-specialization.

The fourth paper “CrowdTravel: scenic spot profiling by using heterogeneous crowdsourced data” by Guo et al. proposes CrowdTravel, a multi-source social media data fusion approach for multi-aspect tourism information perception, which can provide travelling assistance for tourists by crowd intelligence mining. Experiments over a dataset of several popular scenic spots in Beijing and Xi’an, China, indicate that the authors’ approach attains fine-grained characterization for the scenic spots and delivers excellent performance.

The fifth paper “Internet of Things based activity surveillance of defence personnel” by Bhatia et al. presents a comprehensive IoT-based framework for analyzing national integrity of defence personnel with consideration to his/her daily activities. Specifically, Integrity Index Value is defined for every defence personnel based on different social engagements, and activities for detecting the vulnerability to national security. In addition to this, a probabilistic decision tree based automated decision making is presented to aid defence officials in analyzing various activities of a defence personnel for his/her integrity assessment.

The sixth paper “Recommending property with short days-on-market for estate agency” by Mou et al. proposes an estate with short days-on-market appraisal framework to automatically recommend those estates using transaction data and profile information crawled from websites. Both the spatial and temporal characteristics of an estate are integrated into the framework. The results show that the proposed framework can estimate accurately about 78% estates.

The seventh paper “An anonymous data reporting strategy with ensuring incentives for mobile crowd-sensing” by Li et al. proposes a system and a strategy to ensure anonymous data reporting while ensuring incentives simultaneously. The proposed protocol is arranged in five stages that mainly leverage three concepts: (1) slot reservation based on shuffle, (2) data submission based on bulk transfer and multi-player dc-nets, and (3) incentive mechanism based on blind signature.

The last paper “Semantic place prediction from crowd-sensed mobile phone data” by Celik et al. semantically classifes places visited by smart phone users utilizing the data collected from sensors and wireless interfaces available on the phones as well as phone usage patterns, such as battery level, and time-related information, with machine learning algorithms. For this study, the authors collect data from 15 participants at Galatasaray University for 1 month, and try different classification algorithms such as decision tree, random forest, k-nearest neighbour, naive Bayes, and multi-layer perceptron….(More)”.

Improving journeys by opening data: The case of Transport for London (TfL)

Curated on Posted on by Stefaan Verhulst

Merlin Stone and Eleni Aravopoulou in The Bottom Line: “This case study describes how one of the world’s largest public transport operations, Transport for London (TfL), transformed the real-time availability of information for its customers and staff through the open data approach, and what the results of this transformation were. Its purpose is therefore to show what is required for an open data approach to work.

This case study is based mainly on interviews at TfL and data supplied by TfL directly to the researchers. It analyses as far as possible the reported facts of the case, in order to identify the processes required to open data and the benefits thereof.

The main finding is that achieving an open data approach in public transport is helped by having a clear commitment to the idea that the data belongs to the public and that third parties should be allowed to use and repurpose the information, by having a strong digital strategy, and by creating strong partnerships with data management organisations that can support the delivery of high volumes of information.

The case study shows how open data can be used to create commercial and non-commercial customer-facing products and services, which passengers and other road users use to gain a better travel experience, and that this approach can be valued in terms of financial/economic contribution to customers and organisations….(More)”.

The Potential for Human-Computer Interaction and Behavioral Science

Curated on Posted on by Stefaan Verhulst

Article by Kweku Opoku-Agyemang as  part of a special issue by Behavioral Scientist on “Connected State of Mind,” which explores the impact of tech use on our behavior and relationships (complete issue here):

A few days ago, one of my best friends texted me a joke. It was funny, so a few seconds later I replied with the “laughing-while-crying emoji.” A little yellow smiley face with tear drops perched on its eyes captured exactly what I wanted to convey to my friend. No words needed. If this exchange happened ten years ago, we would have emailed each other. Two decades ago, snail mail.

As more of our interactions and experiences are mediated by screens and technology, the way we relate to one another and our world is changing. Posting your favorite emoji may seem superficial, but such reflexes are becoming critical for understanding humanity in the 21st century.

Seemingly ubiquitous computer interfaces—on our phones and laptops, not to mention our cars, coffee makers, thermostats, and washing machines—are blurring the lines between our connected and our unconnected selves. And it’s these relationships, between users and their computers, which define the field of human–computer interaction (HCI). HCI is based on the following premise: The more we understand about human behavior, the better we can design computer interfaces that suit people’s needs.

For instance, HCI researchers are designing tactile emoticons embedded in the Braille system for individuals with visual impairments. They’re also creating smartphones that can almost read your mind—predicting when and where your finger is about to touch them next.

Understanding human behavior is essential for designing human-computer interfaces. But there’s more to it than that: Understanding how people interact with computer interfaces can help us understand human behavior in general.

One of the insights that propelled behavioral science into the DNA of so many disciplines was the idea that we are not fully rational: We procrastinate, forget, break our promises, and change our minds. What most behavioral scientists might not realize is that as they transcended rationality, rational models found a new home in artificial intelligence. Much of A.I. is based on the familiar rational theories that dominated the field of economics prior to the rise of behavioral economics. However, one way to better understand how to apply A.I. in high-stakes scenarios, like self-driving cars, may be to embrace ways of thinking that are less rational.

It’s time for information and computer science to join forces with behavioral science. The mere presence of a camera phone can alter our cognition even when switched off, so if we ignore HCI in behavioral research in a world of constant clicks, avatars, emojis, and now animojis we limit our understanding of human behavior.

Below I’ve outlined three very different cases that would benefit from HCI researchers and behavioral scientists working together: technology in the developing world, video games and the labor market, and online trolling and bullying….(More)”.