Facebook will open its data up to academics to see how it impacts elections

Curated on Posted on by Stefaan Verhulst

MIT Technology Review: “More than 60 researchers from 30 institutions will get access to Facebook user data to study its impact on elections and democracy, and how it’s used by advertisers and publishers.

A vast trove: Facebook will let academics see which websites its users linked to from January 2017 to February 2019. Notably, that means they won’t be able to look at the platform’s impact on the US presidential election in 2016, or on the Brexit referendum in the UK in the same year.

Despite this slightly glaring omission, it’s still hard to wrap your head around the scale of the data that will be shared, given that Facebook is used by 1.6 billion people every day. That’s more people than live in all of China, the most populous country on Earth. It will be one of the largest data sets on human behavior online to ever be released.

The process: Facebook didn’t pick the researchers. They were chosen by the Social Science Research Council, a US nonprofit. Facebook has been working on this project for over a year, as it tries to balance research interests against user privacy and confidentiality.

Privacy: In a blog post, Facebook said it will use a number of statistical techniques to make sure the data set can’t be used to identify individuals. Researchers will be able to access it only via a secure portal that uses a VPN and two-factor authentication, and there will be limits on the number of queries they can each run….(More)”.

Revisiting the causal effect of democracy on long-run development

Curated on Posted on by Stefaan Verhulst

Blog post by Markus Eberhardt: “In a recent paper, Acemoglu et al. (2019), henceforth “ANRR”, demonstrated a significant and large causal effect of democracy on long-run growth. By adopting a simple binary indicator for democracy, and accounting for the dynamics of development, these authors found that a shift to democracy leads to a 20% higher level of development in the long run.1

The findings are remarkable in three ways: 

  1. Previous research often emphasised that a simple binary measure for democracy was perhaps “too blunt a concept” (Persson and Tabellini 2006) to provide robust empirical evidence.
  2.  Positive effects of democracy on growth were typically only a “short-run boost” (Rodrik and Wacziarg 2005). 
  3. The empirical findings are robust across a host of empirical estimators with different assumptions about the data generating process, including one adopting a novel instrumentation strategy (regional waves of democratisation).

ANRR’s findings are important because, as they highlight in a column on Vox, there is “a belief that democracy is bad for economic growth is common in both academic political economy as well as the popular press.” For example, Posner (2010) wrote that “[d]ictatorship will often be optimal for very poor countries”. 

The simplicity of ANRR’s empirical setup, the large sample of countries, the long time horizon (1960 to 2010), and the robust positive – and remarkably stable – results across the many empirical methods they employ send a very powerful message against such doubts that democracy does cause growth.

I agree with their conclusion, but with qualifications. …(More)”.

Computational Social Science of Disasters: Opportunities and Challenges

Curated on Posted on by Stefaan Verhulst

Paper by Annetta Burger, Talha Oz , William G. Kennedy and Andrew T. Crooks: “Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research.

We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research.

Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments….(More)”.

A weather tech startup wants to do forecasts based on cell phone signals

Curated on Posted on by Stefaan Verhulst

Douglas Heaven at MIT Technology Review: “On 14 April more snow fell on Chicago than it had in nearly 40 years. Weather services didn’t see it coming: they forecast one or two inches at worst. But when the late winter snowstorm came it caused widespread disruption, dumping enough snow that airlines had to cancel more than 700 flights across all of the city’s airports.

One airline did better than most, however. Instead of relying on the usual weather forecasts, it listened to ClimaCell – a Boston-based “weather tech” start-up that claims it can predict the weather more accurately than anyone else. According to the company, its correct forecast of the severity of the coming snowstorm allowed the airline to better manage its schedules and minimize losses due to delays and diversions. 

Founded in 2015, ClimaCell has spent the last few years developing the technology and business relationships that allow it to tap into millions of signals from cell phones and other wireless devices around the world. It uses the quality of these signals as a proxy for local weather conditions, such as precipitation and air quality. It also analyzes images from street cameras. It is offering a weather forecasting service to subscribers that it claims is 60 percent more accurate than that of existing providers, such as NOAA.

The internet of weather

The approach makes sense, in principle. Other forecasters use proxies, such as radar signals. But by using information from millions of everyday wireless devices, ClimaCell claims it has a far more fine-grained view of most of the globe than other forecasters get from the existing network of weather sensors, which range from ground-based devices to satellites. (ClimaCell also taps into these, too.)…(More)”.

How Technology Could Revolutionize Refugee Resettlement

Curated on Posted on by Stefaan Verhulst

Krishnadev Calamur in The Atlantic: “… For nearly 70 years, the process of interviewing, allocating, and accepting refugees has gone largely unchanged. In 1951, 145 countries came together in Geneva, Switzerland, to sign the Refugee Convention, the pact that defines who is a refugee, what refugees’ rights are, and what legal obligations states have to protect them.

This process was born of the idealism of the postwar years—an attempt to make certain that those fleeing war or persecution could find safety so that horrific moments in history, such as the Holocaust, didn’t recur. The pact may have been far from perfect, but in successive years, it was a lifeline to Afghans, Bosnians, Kurds, and others displaced by conflict.

The world is a much different place now, though. The rise of populism has brought with it a concomitant hostility toward immigrants in general and refugees in particular. Last October, a gunman who had previously posted anti-Semitic messages online against HIAS killed 11 worshippers in a Pittsburgh synagogue. Many of the policy arguments over resettlement have shifted focus from humanitarian relief to security threats and cost. The Trump administration has drastically cut the number of refugees the United States accepts, and large parts of Europe are following suit.

If it works, Annie could change that dynamic. Developed at Worcester Polytechnic Institute in Massachusetts, Lund University in Sweden, and the University of Oxford in Britain, the software uses what’s known as a matching algorithm to allocate refugees with no ties to the United States to their new homes. (Refugees with ties to the United States are resettled in places where they have family or community support; software isn’t involved in the process.)

Annie’s algorithm is based on a machine learning model in which a computer is fed huge piles of data from past placements, so that the program can refine its future recommendations. The system examines a series of variables—physical ailments, age, levels of education and languages spoken, for example—related to each refugee case. In other words, the software uses previous outcomes and current constraints to recommend where a refugee is most likely to succeed. Every city where HIAS has an office or an affiliate is given a score for each refugee. The higher the score, the better the match.

This is a drastic departure from how refugees are typically resettled. Each week, HIAS and the eight other agencies that allocate refugees in the United States make their decisions based largely on local capacity, with limited emphasis on individual characteristics or needs….(More)”.

AI & Global Governance: Robots Will Not Only Wage Future Wars but also Future Peace

Curated on Posted on by Stefaan Verhulst

Daanish Masood & Martin Waehlisch at the United Nations University: “At the United Nations, we have been exploring completely different scenarios for AI: its potential to be used for the noble purposes of peace and security. This could revolutionize the way of how we prevent and solve conflicts globally.

Two of the most promising areas are Machine Learning and Natural Language Processing. Machine Learning involves computer algorithms detecting patterns from data to learn how to make predictions and recommendations. Natural Language Processing involves computers learning to understand human languages.

At the UN Secretariat, our chief concern is with how these emerging technologies can be deployed for the good of humanity to de-escalate violence and increase international stability.

This endeavor has admirable precedent. During the Cold War, computer scientists used multilayered simulations to predict the scale and potential outcome of the arms race between the East and the West.

Since then, governments and international agencies have increasingly used computational models and advanced Machine Learning to try to understand recurrent conflict patterns and forecast moments of state fragility.

But two things have transformed the scope for progress in this field.

The first is the sheer volume of data now available from what people say and do online. The second is the game-changing growth in computational capacity that allows us to crunch unprecedented, inconceivable quantities data with relative speed and ease.

So how can this help the United Nations build peace? Three ways come to mind.

Firstly, overcoming cultural and language barriers. By teaching computers to understand human language and the nuances of dialects, not only can we better link up what people write on social media to local contexts of conflict, we can also more methodically follow what people say on radio and TV. As part of the UN’s early warning efforts, this can help us detect hate speech in a place where the potential for conflict is high. This is crucial because the UN often works in countries where internet coverage is low, and where the spoken languages may not be well understood by many of its international staff.

Natural Language Processing algorithms can help to track and improve understanding of local debates, which might well be blind spots for the international community. If we combine such methods with Machine Learning chatbots, the UN could conduct large-scale digital focus groups with thousands in real-time, enabling different demographic segments in a country to voice their views on, say, a proposed peace deal – instantly testing public support, and indicating the chances of sustainability.

Secondly, anticipating the deeper drivers of conflict. We could combine new imaging techniques – whether satellites or drones – with automation. For instance, many parts of the world are experiencing severe groundwater withdrawal and water aquifer depletion. Water scarcity, in turn, drives conflicts and undermines stability in post-conflict environments, where violence around water access becomes more likely, along with large movements of people leaving newly arid areas.

One of the best predictors of water depletion is land subsidence or sinking, which can be measured by satellite and drone imagery. By combining these imaging techniques with Machine Learning, the UN can work in partnership with governments and local communities to anticipate future water conflicts and begin working proactively to reduce their likelihood.

Thirdly, advancing decision making. In the work of peace and security, it is surprising how many consequential decisions are still made solely on the basis of intuition.

Yet complex decisions often need to navigate conflicting goals and undiscovered options, against a landscape of limited information and political preference. This is where we can use Deep Learning – where a network can absorb huge amounts of public data and test it against real-world examples on which it is trained while applying with probabilistic modeling. This mathematical approach can help us to generate models of our uncertain, dynamic world with limited data.

With better data, we can eventually make better predictions to guide complex decisions. Future senior peace envoys charged with mediating a conflict would benefit from such advances to stress test elements of a peace agreement. Of course, human decision-making will remain crucial, but would be informed by more evidence-driven robust analytical tools….(More)”.

Politics and Technology in the Post-Truth Era

Curated on Posted on by Stefaan Verhulst

Book edited by Anna Visvizi and Miltiadis D. Lytras: “Advances in information and communication technology (ICT) have directly impacted the way in which politics operates today. Bringing together research on Europe, the US, South America, the Middle East, Asia and Africa, this book examines the relationship between ICT and politics in a global perspective.

Technological innovations such as big data, data mining, sentiment analysis, cognitive computing, artificial intelligence, virtual reality, augmented reality, social media and blockchain technology are reshaping the way ICT intersects with politics and in this collection contributors examine these developments, demonstrating their impact on the political landscape. Chapters examine topics such as cyberwarfare and propaganda, post-Soviet space, Snowden, US national security, e-government, GDPR, democratization in Africa and internet freedom.


Providing an overview of new research on the emerging relationship between the promise and potential inherent in ICT and its impact on politics, this edited collection will prove an invaluable text for students, researchers and practitioners working in the fields of Politics, International Relations and Computer Science…..(More)”

Introducing the Contractual Wheel of Data Collaboration

Curated on Posted on by Stefaan Verhulst

Blog by Andrew Young and Stefaan Verhulst: “Earlier this year we launched the Contracts for Data Collaboration (C4DC) initiative — an open collaborative with charter members from The GovLab, UN SDSN Thematic Research Network on Data and Statistics (TReNDS), University of Washington and the World Economic Forum. C4DC seeks to address the inefficiencies of developing contractual agreements for public-private data collaboration by informing and guiding those seeking to establish a data collaborative by developing and making available a shared repository of relevant contractual clauses taken from existing legal agreements. Today TReNDS published “Partnerships Founded on Trust,” a brief capturing some initial findings from the C4DC initiative.

The Contractual Wheel of Data Collaboration [beta]

The Contractual Wheel of Data Collaboration [beta] — Stefaan G. Verhulst and Andrew Young, The GovLab

As part of the C4DC effort, and to support Data Stewards in the private sector and decision-makers in the public and civil sectors seeking to establish Data Collaboratives, The GovLab developed the Contractual Wheel of Data Collaboration [beta]. The Wheel seeks to capture key elements involved in data collaboration while demystifying contracts and moving beyond the type of legalese that can create confusion and barriers to experimentation.

The Wheel was developed based on an assessment of existing legal agreements, engagement with The GovLab-facilitated Data Stewards Network, and analysis of the key elements of our Data Collaboratives Methodology. It features 22 legal considerations organized across 6 operational categories that can act as a checklist for the development of a legal agreement between parties participating in a Data Collaborative:…(More)”.

Cyberdiplomacy: Managing Security and Governance Online

Curated on Posted on by Stefaan Verhulst

Book by Shaun Riordan: “The world has been sleep-walking into cyber chaos. The spread of misinformation via social media and the theft of data and intellectual property, along with regular cyberattacks, threaten the fabric of modern societies. All the while, the Internet of Things increases the vulnerability of computer systems, including those controlling critical infrastructure. What can be done to tackle these problems? Does diplomacy offer ways of managing security and containing conflict online?

In this provocative book, Shaun Riordan shows how traditional diplomatic skills and mindsets can be combined with new technologies to bring order and enhance international cooperation. He explains what cyberdiplomacy means for diplomats, foreign services and corporations and explores how it can be applied to issues such as internet governance, cybersecurity, cybercrime and information warfare. Cyberspace, he argues, is too important to leave to technicians. Using the vital tools offered by cyberdiplomacy, we can reduce the escalation and proliferation of cyberconflicts by proactively promoting negotiation and collaboration online….(More)”.

Illuminating Big Data will leave governments in the dark

Curated on Posted on by Stefaan Verhulst

Robin Wigglesworth in the Financial Times: “Imagine a world where interminable waits for backward-looking, frequently-revised economic data seem as archaically quaint as floppy disks, beepers and a civil internet. This fantasy realm may be closer than you think.

The Bureau of Economic Analysis will soon publish its preliminary estimate for US economic growth in the first three months of the year, finally catching up on its regular schedule after a government shutdown paralysed the agency. But other data are still delayed, and the final official result for US gross domestic product won’t be available until July. Along the way there are likely to be many tweaks.

Collecting timely and accurate data are a Herculean task, especially for an economy as vast and varied as the US’s. But last week’s World Bank-International Monetary Fund’s annual spring meetings offered some clues on a brighter, more digital future for economic data.

The IMF hosted a series of seminars and discussions exploring how the hot new world of Big Data could be harnessed to produce more timely economic figures — and improve economic forecasts. Jiaxiong Yao, an IMF official in its African department, explained how it could use satellites to measure the intensity of night-time lights, and derive a real-time gauge of economic health.

“If a country gets brighter over time, it is growing. If it is getting darker then it probably needs an IMF programme,” he noted. Further sessions explored how the IMF could use machine learning — a popular field of artificial intelligence — to improve its influential but often faulty economic forecasts; and real-time shipping data to map global trade flows.

Sophisticated hedge funds have been mining some of these new “alternative” data sets for some time, but statistical agencies, central banks and multinational organisations such as the IMF and the World Bank are also starting to embrace the potential.

The amount of digital data around the world is already unimaginably vast. As more of our social and economic activity migrates online, the quantity and quality is going to increase exponentially. The potential is mind-boggling. Setting aside the obvious and thorny privacy issues, it is likely to lead to a revolution in the world of economic statistics. …

Yet the biggest issues are not the weaknesses of these new data sets — all statistics have inherent flaws — but their nature and location.

Firstly, it depends on the lax regulatory and personal attitudes towards personal data continuing, and there are signs of a (healthy) backlash brewing.

Secondly, almost all of this alternative data is being generated and stored in the private sector, not by government bodies such as the Bureau of Economic Analysis, Eurostat or the UK’s Office for National Statistics.

Public bodies are generally too poorly funded to buy or clean all this data themselves, meaning hedge funds will benefit from better economic data than the broader public. We might, in fact, need legislation mandating that statistical agencies receive free access to any aggregated private sector data sets that might be useful to their work.

That would ensure that our economic officials and policymakers don’t fly blind in an increasingly illuminated world….(More)”.