Stefaan Verhulst

The internet is the answer to all the questions of our time

Curated on July 12, 2015August 3, 2018 by Stefaan Verhulst

Cory Doctorow in The Guardian: “…Why do people work for these organisations? Because they are utopians. Not utopians in the sense of believing that the internet is predestined to come out all right no matter what. Rather, we are utopians because, on the one hand, we are terrified of what kind of surveillance and control the internet enables, and because, on the other hand, we believe that the future is up for grabs: that we can work together to change what the internet is and what it will become. Nothing is more utopian than a belief that, when things are bad, we can make them better.

The internet has become the nervous system of the 21st century, wiring together devices that we carry, devices that are in our bodies, devices that our bodies are in. It is woven into the fabric of government service delivery, of war-fighting systems, of activist groups, of major corporations and teenagers’ social groups and the commerce of street-market hawkers.

There are many fights more important than the fight over how the internet is regulated. Equity in race, gender, sexual preference; the widening wealth gap; the climate crisis – each one far more important than the fight over the rules for the net.

Except for one thing: the internet is how every one of these fights will be won or lost. Without a free, fair and open internet, proponents of urgent struggles for justice will be outmaneuvered and outpaced by their political opponents, by the power-brokers and reactionaries of the status quo. The internet isn’t the most important fight we have; but it’s the most foundational….

The questions of the day are “How do we save the planet from the climate crisis?” and “What do we do about misogyny, racial profiling and police violence, and homophobic laws?” and “How do we check mass surveillance and the widening power of the state?” and “How do we bring down autocratic, human-rights-abusing regimes without leaving behind chaos and tragedy?”

Those are the questions.

But the internet is the answer. If you propose to fix any of these things without using the internet, you’re not being serious. And if you want to free the internet to use in all those fights, there’s a quarter century’s worth of Internet Utopians who’ve got your back….(More)

The data or the hunch

Curated on July 12, 2015December 11, 2018 by Stefaan Verhulst

Ian Leslie at Intelligent Life: “THE GIFT FOR talent-spotting is mysterious, highly prized and celebrated. We love to hear stories about the baseball coach who can spot the raw ability of an erratic young pitcher, the boss who sees potential in the guy in the post room, the director who picks a soloist out of the chorus line. Talent shows are a staple of the TV schedules. We like to believe that certain people—sometimes ourselves—can just sense when a person has something special. But there is another method of spotting talent which doesn’t rely on hunches. In place of intuition, it offers data and analysis. Rather than relying on the gut, it invites us to use our heads. It tends not to make for such romantic stories, but it is effective—which is why, despite our affection, the hunch is everywhere in retreat.

Strike one against the hunch was the publication of “Moneyball” by Michael Lewis (2003), which has attained the status of a management manual for many in sport and beyond. Lewis reported on a cash-strapped major-league baseball team, the Oakland A’s, who enjoyed unlikely success against bigger and better-funded competitors. Their secret sauce was data. Their general manager, Billy Beane, had realised that when it came to evaluating players, the gut instincts of experienced baseball scouts were unreliable, and he employed statisticians to identify talent overlooked by the big clubs…..

These days, when a football club is interested in a player, it considers the average distance he runs in a game, the number of passes and tackles or blocks he makes, his shots on goal, the ratio of goals to shots, and many other details nobody thought to measure a generation ago. Sport is far from the only industry in which talent-spotting is becoming a matter of measurement. Prithwijit Mukerji, a postgraduate at the University of Westminster in London, recently published a paper on the way the music industry is being transformed by “the Moneyball approach”. By harvesting data from Facebook and Twitter and music services like Spotify and Shazam, executives can track what we are listening to in far more detail than ever before, and use it as a guide to what we will listen to next….

This is the day of the analyst. In education, academics are working their way towards a reliable method of evaluating teachers, by running data on test scores of pupils, controlled for factors such as prior achievement and raw ability. The methodology is imperfect, but research suggests that it’s not as bad as just watching someone teach. A 2011 study led by Michael Strong at the University of California identified a group of teachers who had raised student achievement and a group who had not. They showed videos of the teachers’ lessons to observers and asked them to guess which were in which group. The judges tended to agree on who was effective and ineffective, but, 60% of the time, they were wrong. They would have been better off flipping a coin. This applies even to experts: the Gates Foundation funded a vast study of lesson observations, and found that the judgments of trained inspectors were highly inconsistent.

THE LAST STRONGHOLD of the hunch is the interview. Most employers and some universities use interviews when deciding whom to hire or admit. In a conventional, unstructured interview, the candidate spends half an hour or so in a conversation directed at the whim of the interviewer. If you’re the one deciding, this is a reassuring practice: you feel as if you get a richer impression of the person than from the bare facts on their résumé, and that this enables you to make a better decision. The first theory may be true; the second is not.

Decades of scientific evidence suggest that the interview is close to useless as a tool for predicting how someone will do a job. Study after study has found that organisations make better decisions when they go by objective data, like the candidate’s qualifications, track record and performance in tests. “The assumption is, ‘if I meet them, I’ll know’,” says Jason Dana, of Yale School of Management, one of many scholars who have looked into the interview’s effectiveness. “People are wildly over-confident in their ability to do this, from a short meeting.” When employers adopt a holistic approach, combining the data with hunches formed in interviews, they make worse decisions than they do going on facts alone….” (More)

Collaborative Innovation

Curated on July 11, 2015December 11, 2018 by Stefaan Verhulst

Book by Mitsuru Kodama on “Developing Health Support Ecosystems…With the development of the aging society and the increased importance of emergency risk management in recent years, a large number of medical care challenges – advancing medical treatments, care & support, pharmacological treatments, greater health awareness, emergency treatments, telemedical treatment and care, the introduction of electronic charts, and rising costs – are emerging as social issues throughout the whole world. Hospitals and other medical institutions must develop and maintain superior management to achieve systems that can provide better medical care, welfare and health while enabling “support innovation.” Key medical care, welfare and health industries play a crucial role in this, but also of importance are management innovation models that enable “collaborative innovation” by closely linking diverse fields such as ICT, energy, electric equipment, machinery and transport.

Looking across different industries, Collaborative Innovation offers new knowledge and insights on the extraordinary value and increasing necessity of collaboration across different organizations in improving the health and lives of people. It breaks new ground with its research theme of building “health support ecosystems,” focusing on protecting people through collaborative innovation. This book opens up new, wide-ranging interdisciplinary academic research domains combining the humanities with science across various areas including general business administration, economics, information technology, medical informatics and drug information science….(More)”

Crowdsourcing Solutions for Disaster Response: Examples and Lessons for the US Government

Curated on July 11, 2015August 3, 2018 by Stefaan Verhulst

Paper by David Becker, and Samuel Bendett in Procedia Engineering: “Crowdsourcing has become a quick and efficient way to solve a wide variety of problems – technical solutions, social and economic actions, fundraising and troubleshooting of numerous issues that affect both the private and the public sectors. US government is now actively using crowdsourcing to solve complex problems that previously had to be handled by a limited circle of professionals. This paper outlines several examples of how a Department of Defense project headquartered at the National Defense University is using crowdsourcing for solutions to disaster response problems….(More)”

Using Twitter as a data source: An overview of current social media research tools

Curated on July 11, 2015December 11, 2018 by Stefaan Verhulst

Wasim Ahmed at the LSE Impact Blog: “I have a social media research blog where I find and write about tools that can be used to capture and analyse data from social media platforms. My PhD looks at Twitter data for health, such as the Ebola outbreak in West Africa. I am increasingly asked why I am looking at Twitter, and what tools and methods there are of capturing and analysing data from other platforms such as Facebook, or even less traditional platforms such as Amazon book reviews. Brainstorming a couple of responses to this question by talking to members of the New Social Media New Social Science network, there are at least six reasons:

Twitter is a popular platform in terms of the media attention it receives and it therefore attracts more research due to its cultural status
Twitter makes it easier to find and follow conversations (i.e., by both its search feature and by tweets appearing in Google search results)
Twitter has hashtag norms which make it easier gathering, sorting, and expanding searches when collecting data
Twitter data is easy to retrieve as major incidents, news stories and events on Twitter are tend to be centred around a hashtag
The Twitter API is more open and accessible compared to other social media platforms, which makes Twitter more favourable to developers creating tools to access data. This consequently increases the availability of tools to researchers.
Many researchers themselves are using Twitter and because of their favourable personal experiences, they feel more comfortable with researching a familiar platform.

It is probable that a combination of response 1 to 6 have led to more research on Twitter. However, this raises another distinct but closely related question: when research is focused so heavily on Twitter, what (if any) are the implications of this on our methods?

As for the methods that are currently used in analysing Twitter data i.e., sentiment analysis, time series analysis (examining peaks in tweets), network analysis etc., can these be applied to other platforms or are different tools, methods and techniques required? In addition to qualitative methods such as content analysis, I have used the following four methods in analysing Twitter data for the purposes of my PhD, below I consider whether these would work for other social media platforms:

Sentiment analysis works well with Twitter data, as tweets are consistent in length (i.e., <= 140) would sentiment analysis work well with, for example Facebook data where posts may be longer?
Time series analysis is normally used when examining tweets overtime to see when a peak of tweets may occur, would examining time stamps in Facebook posts, or Instagram posts, for example, produce the same results? Or is this only a viable method because of the real-time nature of Twitter data?
Network analysis is used to visualize the connections between people and to better understand the structure of the conversation. Would this work as well on other platforms whereby users may not be connected to each other i.e., public Facebook pages?
Machine learning methods may work well with Twitter data due to the length of tweets (i.e., <= 140) but would these work for longer posts and for platforms that are not text based i.e., Instagram?

It may well be that at least some of these methods can be applied to other platforms, however they may not be the best methods, and may require the formulation of new methods, techniques, and tools.

So, what are some of the tools available to social scientists for social media data? In the table below I provide an overview of some the tools I have been using (which require no programming knowledge and can be used by social scientists):…(More)”

Transforming City Governments for Successful Smart Cities

Curated on July 11, 2015December 11, 2018 by Stefaan Verhulst

New book edited by Rodríguez-Bolívar, Manuel Pedro: “There has been much attention paid to the idea of Smart Cities as researchers have sought to define and characterize the main aspects of the concept, including the role of creative industries in urban growth, the importance of social capital in urban development, and the role of urban sustainability. This book develops a critical view of the Smart City concept, the incentives and role of governments in promoting the development of Smart Cities and the analysis of experiences of e-government projects addressed to enhance Smart Cities. This book further analyzes the perceptions of stakeholders, such as public managers or politicians, regarding the incentives and role of governments in Smart Cities and the critical analysis of e-government projects to promote Smart Cities’ development, making the book valuable to academics, researchers, policy-makers, public managers, international organizations and technical experts in understanding the role of government to enhance Smart Cities’ projects….(More)”

White House to make public records more public

Curated on July 11, 2015December 11, 2018 by Stefaan Verhulst

Lisa Rein at the Washington Post: “The law that’s supposed to keep citizens in the know about what their government is doing is about to get more robust.

Starting this week, seven agencies — including the Environmental Protection Agency and the Office of the Director of National Intelligence — launched a new effort to put online the records they distribute to requesters under the Freedom of Information Act (FOIA).

So if a journalist, nonprofit group or corporation asks for the records, what they see, the public also will see. Documents still will be redacted where necessary to protect what the government decides is sensitive information, an area that’s often disputed but won’t change with this policy.

The Obama administration’s new Open Government initiative began quietly on the agencies’ Web sites days after FOIA’s 49th anniversary. It’s a response to years of pressure from open-government groups and lawmakers to boost public access to records of government decisions, deliberations and policies.

The “release to one is release to all” policy will start as a six-month pilot at the EPA, the Office of the Director of National Intelligence, the Millennium Challenge Corporation and within some offices at the Department of Homeland Security, the Defense Department, the Justice Department and the National Archives and Records Administration….(More)”

Democratising the Data Revolution

Curated on July 11, 2015August 3, 2018 by Stefaan Verhulst

Jonathan Gray at Open Knowledge: “What will the “data revolution” do? What will it be about? What will it count? What kinds of risks and harms might it bring? Whom and what will it serve? And who will get to decide?

Today we are launching a new discussion paper on “Democratising the Data Revolution”, which is intended to advance thinking and action around civil society engagement with the data revolution. It looks beyond the disclosure of existing information, towards more ambitious and substantive forms of democratic engagement with data infrastructures.¹

It concludes with a series of questions about what practical steps institutions and civil society organisations might take to change what is measured and how, and how these measurements are put to work.

You can download the full PDF report here, or continue to read on in this blog post.

What Counts?

How might civil society actors shape the data revolution? In particular, how might they go beyond the question of what data is disclosed towards looking at what is measured in the first place? To kickstart discussion around this topic, we will look at three kinds of intervention: changing existing forms of measurement, advocating new forms of measurement and undertaking new forms of measurement.

Changing Existing Forms of Measurement

Rather than just focusing on the transparency, disclosure and openness of public information, civil society groups can argue for changing what is measured with existing data infrastructures. One example of this is recent campaigning around company ownership in the UK. Advocacy groups wanted to unpick networks of corporate ownership and control in order to support their campaigning and investigations around tax avoidance, tax evasion and illicit financial flows.

While the UK company register recorded information about “nominal ownership”, it did not include information about so-called “beneficial ownership”, or who ultimately benefits from the ownership and control of companies. Campaigners undertook an extensive programme of activities to advocate for changes and extensions to existing data infrastructures – including via legislation, software systems, and administrative protocols.²

Advocating New Forms of Measurement

As well as changing or recalibrating existing forms of measurement, campaigners and civil society organisations can make the case for the measurement of things which were not previously measured. For example, over the past several decades social and political campaigning has resulted in new indicators about many different issues – such as gender inequality, health, work, disability, pollution or education.³ In such cases activists aimed to establish a given indicator as important and relevant for public institutions, decision makers, and broader publics – in order to, for example, inform policy development or resource allocation.

Undertaking New Forms of Measurement

Historically, many civil society organisations and advocacy groups have collected their own data to make the case for action on issues that they work on – from human rights abuses to endangered species….(More)”