Facebook Data for Good


Foreword by Sheryl Sandberg: “When Facebook launched the Data for Good program in 2017, we never imagined it would play a role so soon in response to a truly global emergency. The COVID-19 pandemic is not just a public health crisis, but also a social and economic one. It has caused hardship in every part of the world, but its impact hasn’t been felt equally. It has hit women and the most disadvantaged communities the hardest – something this work has helped shine a light on.

In response to the pandemic, Facebook has been part of an unprecedented collaboration between technology companies, the public sector, universities, nonprofits and others. Our partners operate in some of the most challenging environments in the world, where lengthy analysis and debate is often a luxury they don’t have. The policies that govern delivery of vaccines, masks, and financial support can mean the difference between life and death. By sharing tools that provide real-time insights, Facebook can make decision-making on the ground just a little bit easier and more effective.

This report highlights some of the ways Facebook data – shared in a way that protects the privacy of individuals – assisted the response efforts to the pandemic and other major crises in 2020. I hope the examples included help illustrate what successful data sharing projects can look like, and how future projects can be improved. Above all, I hope we can continue to work together in 2021 and beyond to save lives and mitigate the damage caused by the pandemic and any crises that may follow….(More)”.

Enabling the future of academic research with the Twitter API


Twitter Developer Blog: “When we introduced the next generation of the Twitter API in July 2020, we also shared our plans to invest in the success of the academic research community with tailored solutions that better serve their goals. Today, we’re excited to launch the Academic Research product track on the new Twitter API. 

Why we’re launching this & how we got here

Since the Twitter API was first introduced in 2006, academic researchers have used data from the public conversation to study topics as diverse as the conversation on Twitter itself – from state-backed efforts to disrupt the public conversation to floods and climate change, from attitudes and perceptions about COVID-19 to efforts to promote healthy conversation online. Today, academic researchers are one of the largest groups of people using the Twitter API. 

Our developer platform hasn’t always made it easy for researchers to access the data they need, and many have had to rely on their own resourcefulness to find the right information. Despite this, for over a decade, academic researchers have used Twitter data for discoveries and innovations that help make the world a better place.

Over the past couple of years, we’ve taken iterative steps to improve the experience for researchers, like when we launched a webpage dedicated to Academic Research, and updated our Twitter Developer Policy to make it easier to validate or reproduce others’ research using Twitter data.

We’ve also made improvements to help academic researchers use Twitter data to advance their disciplines, answer urgent questions during crises, and even help us improve Twitter. For example, in April 2020, we released the COVID-19 stream endpoint – the first free, topic-based stream built solely for researchers to use data from the global conversation for the public good. Researchers from around the world continue to use this endpoint for a number of projects.

Over two years ago, we started our own extensive research to better understand the needs, constraints and challenges that researchers have when studying the public conversation. In October 2020, we tested this product track in a private beta program where we gathered additional feedback. This gave us a glimpse into some of the important work that the free Academic Research product track we’re launching today can now enable….(More)”.

Facebook will let researchers study how advertisers targeted users with political ads prior to Election Day


Nick Statt at The Verge: “Facebook is aiming to improve transparency around political advertising on its platform by opening up more data to independent researchers, including targeting information on more than 1.3 million ads that ran in the three months prior to the US election on November 3rd of last year. Researchers interested in studying the ads can apply for access to the Facebook Open Research and Transparency (FORT) platform here.

The move is significant because Facebook has long resisted willfully allowing access to data around political advertising, often citing user privacy. The company has gone so far as to even disable third-party web plugins, like ProPublica’s Facebook Political Ad Collector tool, that collect such data without Facebook’s express consent.

Numerous research groups around the globe have spent years now studying Facebook’s impact on everything from democratic elections to news dissemination, but sometimes without full access to all the desired data. Only last year, after partnering with Harvard University’s Social Science One (the group overseeing applications for the new political ad targeting initiative), did Facebook better formalize the process of granting anonymized user data for research studies.

In the past, Facebook has made some crucial political ad information in its Ad Library available to the public, including the amount spent on certain ads and demographic information about who saw those ads. But now the company says it wants to do more to improve transparency, specifically around how advertisers target certain subsets of users with political advertising….(More)”.

Twitter’s misinformation problem is much bigger than Trump. The crowd may help solve it.


Elizabeth Dwoskin at the Washington Post: “A pilot program called Birdwatch lets selected users write corrections and fact checks on potentially misleading tweets…

The presidential election is over, but the fight against misinformation continues.

The latest volley in that effort comes from Twitter, which on MondayannouncedBirdwatch, a pilot project that uses crowdsourcing techniques to combat falsehoods and misleading statements on its service.

The pilot, which is open to only about 1,000 select users who can apply to be contributors, will allow people to write notes with corrections and accurate information directly into misleading tweets — a method that has the potential to get quality information to people more quickly than traditional fact-checking. Fact checks that are rated by other contributors as high quality may get bumped up or rewarded with greater visibility.

Birdwatch represents Twitter’s most experimental response to one of the biggest lessons that social media companies drew from the historic events of 2020: that their existing efforts to combat misinformation — including labeling, fact-checking and sometimes removing content — were not enough to prevent falsehoods about a stolen election or the coronavirus from reaching and influencing broad swaths of the population. Researchers who studied enforcement actions by social media companies last year found that fact checks and labels are usually implemented too late, after a post or a tweet has gone viral.

The Birdwatch project — which for the duration of the pilot will function as a separate website — is novel in that it attempts to build new mechanisms into Twitter’s product that foreground fact-checking by its community of 187 million daily users worldwide. Rather than having to comb through replies to tweets to sift through what’s true or false — or having Twitter employees append to a tweet a label providing additional context — users will be able to click on a separate notes folder attached to a tweet where they can see the consensus-driven responses from the community. Twitter will have a team reviewing winning responses to prevent manipulation, though a major question is whether any part of the process will be automated and therefore more easily gamed….(More)”

Consensus or chaos? Pandemic response hinges on trust, experts say


Article by Catherine Cheney: “Trust is a key reason for the wide variance in how countries have fared during the COVID-19 pandemic, determining why some have succeeded in containing the virus while others have failed, according to new research on responses across 23 countries.

The work, supported by Schmidt Futures and the National Science Foundation and carried out by teams at Columbia, Harvard, and Cornell Universities, studied national responses to COVID-19 based on public health, economy, and politics.

It organizes countries into three categories: control, consensus, and chaos. The researchers call the United States the leading example of high levels of polarization, decentralized decision-making, and distrust in expertise leading to policy chaos. The category also includes Brazil, India, Italy, and the United Kingdom.

To prepare for future pandemics, countries must build trust in public health, government institutions, and expert advice, according to a range of speakers at last week’s Futures Forum on Preparedness. Schmidt Futures, which co-hosted the event, announced that it is launching a new challenge to source the best ideas from around the world for developing trust in public health interventions. This request for proposals is likely just the beginning as funders explore how to learn from the pandemic and build trust moving forward….(More)”.

We need a new era of international data diplomacy


Rohinton P. Medhora at the Financial Times: “From contact-tracing apps to telemedicine, digital health innovations that can help tackle coronavirus have been adopted swiftly during the pandemic. Lagging far behind, however, are any investigations of their reliability and the implications for privacy and human rights.

In the wake of this surge in “techno-solutionism”, the world needs a new era of data diplomacy to catch up.

Big data holds great promise in improving health outcomes. But it requires norms and standards to govern collection, storage and use, for which there is no global consensus. 

The world broadly comprises four data zones — China, the US, the EU and the remainder. The state-centric China zone, where individuals have no control over their personal data, is often portrayed as the poster child of the long-threatened Orwellian society.A woman scans a QR code of a local app to track personal data for the Covid-19 containment in Zouping in east China’s Shandong province © Barcroft Media via Getty Images

Yet the corporation-centric US zone is also disempowering. The “consent” that users provide to companies is meaningless. Most consumers do not read the endless pages of fine print before “agreeing”, while not consenting means opting out of the digital world and is seldom useful.

The EU’s General Data Protection Regulation goes furthest in entrenching the rights of EU citizens to safeguard their privacy and provide a measure of control over personal data.

But it is not without drawbacks. Costs of compliance are high, with small and medium-sized companies facing a disproportionately large bill that strengthens the large companies that the regulation was designed to rein in. There are also varying interpretations of the rules by different national data protection authorities.

The rest of the world does not have the capacity to create meaningful data governance. Governments are either de facto observers of others’ rules or stumble along with a non-regime. One-fifth of countries have no data protection and privacy legislation, according to figures from Unctad, the UN’s trade and development agency.

Global diplomacy is needed to bring some harmony in norms and practices between these four zones, but the task is not easy. Data straddles our prosperity, health, commerce, quality of democracy, security and safety.

A starting point could be a technology charter of principles, such as the Universal Declaration of Human Rights. It may not be fully applied everywhere, but it could serve as a beacon of hope — particularly for citizens in countries with oppressive regimes — and could guide the drafting of national and subnational legislation.

A second focus should be the equitable taxation of multinational digital platforms that use canny accounting practices to cut their tax bill. While the largest share of users — and one that is growing fast — are in populous poorer parts of the world, the value created from their data goes to richer countries.

This imbalance, coupled with widespread use of tax havens by multinational technology companies, is exacerbating government funding gaps already under pressure because of the pandemic.

A third priority is to revisit statistics. Just as the UN System of National Accounts was introduced in the 1950s, today we need a set of universally accepted definitions and practices to categorise data.

That would allow us to measure and understand the nature of the new data-driven economy. National statistical agencies must be strengthened to gather information and to act as stewards of ever greater quantities of personal data.

Finally, just as the financial crisis of 2007-08 led to the creation of the Financial Stability Forum (a global panel of regulators now called the Financial Stability Board), the Covid-19 crisis is an opportunity to galvanise action through a digital stability board….(More)”

Chief information officers’ perceptions about artificial intelligence


Article by J. Ignacio Criado et al: “This article presents a study about artificial intelligence (AI) policy based on the perceptions, expectations, and challenges/opportunities given by chief information officers (CIOs). In general, publications about AI in the public sector relies on experiences, cases, ideas, and results from the private sector. Our study stands out from the need of defining a distinctive approach to AI in the public sector, gathering primary (and comparative) data from different countries, and assessing the key role of CIOs to frame federal/national AI policies and strategies. This article reports three research questions, including three dimensions of analysis: (1) perceptions regarding to the concept of AI in the public sector; (2) expectations about the development of AI in the public sector; and, (3) challenges and opportunities of AI in the public sector. This exploratory study presents the results of a survey administered to federal/national ministerial government CIOs in ministries of Mexico and Spain. Our descriptive statistical (and exploratory) analysis provides an overall approach to our dimensions, exploratory answering the research questions of the study. Our data supports the existence of different governance models and policy priorities in different countries. Also, these results might inform research in this same area and will help senior officials to assess the national AI policies actually in process of design and implementation in different national/federal, regional/state, and local/municipal contexts….(More)”.

Anticipatory innovation governance


OECD Working Paper: “This working paper introduces the key concepts and features of anticipatory innovation governance– i.e. the structures and mechanisms to allow and promote anticipatory innovation alongside other types of innovation in the public sector. This paper draws on academic literature and OECD work on a range of areas including public sector innovation, foresight, anticipatory governance and emerging technologies. The paper starts outlining an emerging framework to guide policy making in complex and uncertain contexts and sets out some questions for further research in the area of anticipatory innovation governance….(More)”

How COVID-19 Is Accelerating the Shift Toward a Quantified Society


Essay by Jesse Hirsh: “The COVID-19 pandemic is accelerating global digital transformation and the adoption of digital technologies. It is also enacting a political and cultural shift toward a quantified society: a society in which measurement and predictive modelling dominate (political) decision making, and where surveillance is expansive and pervasive.

While viruses and disease have always been with us, what’s changing is our ability to measure and understand them. This ability comes at a time when globalization (and, by extension, climate change) has transformed the kinds of viruses and diseases we will face.

The knowledge of what can kill us — or is killing us — compels governments and health authorities to both take action in response and gather more data to understand the threat. Like many disasters or other globally impactful events, the COVID-19 pandemic is accelerating the development and implementation of quantification technologies.

Health researchers are now measuring the spread of a virus across the population in ways not previously possible, through the use of a set of data that is ever-growing, especially in countries such as China that have less regard for personal privacy. Canada and the United States are not yet conducting tracking and tracing of infections at a level that would enable containment. This level, however, is due to inadequate staffing rather than insufficient data. Still, the desire for more information remains.

As a result, our ability to measure human health and disease transmission is set to reach new records and capabilities. Through sources ranging from individuals’ use of digital health tools to contact tracing records, health-related data is amassing at a prodigious rate.

What are the impacts or consequences of this dramatic increase in both health data and the perceived value or urgency of that data?…(More)”.

Inaccurate Data, Half-Truths, Disinformation, and Mob Violence


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Image credit: Kayla Velasquez/Unsplash.

Selected Readings by Fiona Cece, Uma Kalkar, and Stefaan Verhulst: “The mob attack on the US Congress was alarming and the result of various efforts to undermine the trust in and legitimacy of longstanding democratic processes and institutions. In particular, the use of inaccurate data, half-truths, and disinformation to spread hate and division is considered a key driver behind last week’s attack. Altering data to support conspiracy theories or challenging and undermining the credibility of trusted data sources to allow for alternative narratives to flourish, if left unchallenged, has consequences — including the increased acceptance and use of violence both off-line and on-line.

Everyone working on data and information needs to be aware of the implications of altering or misusing data (including election results) to support malicious objectives. The January 6th riot is unfortunately not a unique event, nor is it contained to the US. Below, we provide a curation of findings and readings that illustrate the global danger of inaccurate data, half-truths, and willful disinformation….(Readings)”.