media

Humanity 360: World Humanitarian Data and Trends 2015

Curated on January 13, 2016August 15, 2018 by Stefaan Verhulst

OCHA: “WORLD HUMANITARIAN DATA AND TRENDS

Highlights major trends, challenges and opportunities in the nature of humanitarian crises, showing how the humanitarian landscape is evolving in a rapidly changing world.

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LEAVING NO ONE BEHIND: HUMANITARIAN EFFECTIVENESS IN THE AGE OF THE SUSTAINABLE DEVELOPMENT GOALS

Exploring what humanitarian effectiveness means in today’s world ‐ better meeting the needs of people in crisis, better moving people out of crisis.

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TOOLS FOR DATA COORDINATION AND COLLECTION

How Facebook Makes Us Dumber

Curated on January 12, 2016August 3, 2018 by Stefaan Verhulst

Cass R. Sunstein in BloombergView: “Why does misinformation spread so quickly on the social media? Why doesn’t it get corrected? When the truth is so easy to find, why do people accept falsehoods?

A new study focusing on Facebook users provides strong evidence that the explanation is confirmation bias: people’s tendency to seek out information that confirms their beliefs, and to ignore contrary information.

Confirmation bias turns out to play a pivotal role in the creation of online echo chambers. This finding bears on a wide range of issues, including the current presidential campaign, the acceptance of conspiracy theories and competing positions in international disputes.

The new study, led by Michela Del Vicario of Italy’s Laboratory of Computational Social Science, explores the behavior of Facebook users from 2010 to 2014. One of the study’s goals was to test a question that continues to be sharply disputed: When people are online, do they encounter opposing views, or do they create the virtual equivalent of gated communities?

Del Vicario and her coauthors explored how Facebook users spread conspiracy theories (using 32 public web pages); science news (using 35 such pages); and “trolls,” which intentionally spread false information (using two web pages). Their data set is massive: It covers all Facebook posts during the five-year period. They explored which Facebook users linked to one or more of the 69 web pages, and whether they learned about those links from their Facebook friends.

In sum, the researchers find a lot of communities of like-minded people. Even if they are baseless, conspiracy theories spread rapidly within such communities.

More generally, Facebook users tended to choose and share stories containing messages they accept, and to neglect those they reject. If a story fits with what people already believe, they are far more likely to be interested in it and thus to spread it….(More)”

Social Media for Government Services

Curated on January 8, 2016August 3, 2018 by Stefaan Verhulst

Book edited by Surya Nepal, Cécile Paris and Dimitrios Georgakopoulos: “This book highlights state-of-the-art research, development and implementation efforts concerning social media in government services, bringing together researchers and practitioners in a number of case studies. It elucidates a number of significant challenges associated with social media specific to government services, such as: benefits and methods of assessing; usability and suitability of tools, technologies and platforms; governance policies and frameworks; opportunities for new services; integrating social media with organisational business processes; and specific case studies. The book also highlights the range of uses and applications of social media in the government domain, at both local and federal levels. As such, it offers a valuable resource for a broad readership including academic researchers, practitioners in the IT industry, developers, and government policy- and decision-makers….(More)“

Living Labs: Concepts, Tools and Cases

Curated on January 8, 2016July 19, 2019 by Stefaan Verhulst

Introduction by Pieter Ballon , Dimitri Schuurman: “This special issue on “Living labs: concepts, tools and cases” comes 10 years after the first scientific publications that defined the notion of living labs, but more than 15 years after the appearance of the first living lab projects (Ballon et al., 2005; Eriksson et al., 2005). This five-year gap demonstrates the extent to which living labs have been a practice-driven phenomenon. Right up to this day, they represent a pragmatic approach to innovation (of information and communication technologies [ICTs] and other artefacts), characterised by a.o. experimentation in real life and active involvement of users.

While there is now a certain body of literature that attempts to clarify and analyse the concept (Følstad, 2008; Almirall et al., 2012; Leminen et al., 2012), living lab practices are still under-researched, and a theoretical and methodological gap continues to exist in terms of the restricted amount and visibility of living lab literature vis-à-vis the rather large community of practice (Schuurman, 2015). The present special issue aims to assist in filling that gap.

This does not mean that the development of living labs has not been informed by scholarly literature previously (Ballon, 2015). Cornerstones include von Hippel’s (1988) work on user-driven innovation because of its emphasis on the ability of so-called lead users, rather than manufacturers, to create (mainly ICT) innovations. Another cornerstone is Silverstone’s (1993) theory on the domestication of ICTs that frames technology adoption as an ongoing struggle between users and technology where the user attempts to take control of the technological artefact and the technology comes to be fitted to users’ daily routines. It has been said that, in living labs, von Hippel’s concept of user-driven design and Silverstone’s insights into the appropriation of technologies are coupled dynamically through experimentation (Frissen and Van Lieshout, 2006).

The concept of stigmergy, which refers to addressing complex problems by collective, yet uncoordinated, actions and interactions of communities of individuals, has gradually become the third foundational element, as social media have provided online platforms for stigmergic behaviour, which has subsequently been linked to the “spontaneous” emergence of innovations (Pallot et al., 2010; Kiemen and Ballon, 2012). A fourth cornerstone is the literature on open and business model innovation, which argues that today’s fast-paced innovation landscape requires collaboration between multiple business and institutional stakeholders, and that the business should use these joint innovation endeavours to find the right “business architecture” (Chesbrough, 2003; Mitchell and Coles, 2003).….(More)

The Routledge Companion to Social Media and Politics

Curated on December 26, 2015August 15, 2018 by Stefaan Verhulst

Book edited by Axel Bruns, Gunn Enli, Eli Skogerbo, Anders Olof Larsson, Christian Christensen: “Social media are now widely used for political protests, campaigns, and communication in developed and developing nations, but available research has not yet paid sufficient attention to experiences beyond the US and UK. This collection tackles this imbalance head-on, compiling cutting-edge research across six continents to provide a comprehensive, global, up-to-date review of recent political uses of social media.

Drawing together empirical analyses of the use of social media by political movements and in national and regional elections and referenda, The Routledge Companion to Social Media and Politics presents studies ranging from Anonymous and the Arab Spring to the Greek Aganaktismenoi, and from South Korean presidential elections to the Scottish independence referendum. The book is framed by a selection of keystone theoretical contributions, evaluating and updating existing frameworks for the social media age….(More)”

Privacy in Public Spaces: What Expectations of Privacy Do We Have in Social Media Intelligence?

Curated on December 16, 2015August 3, 2018 by Stefaan Verhulst

Paper by Edwards, Lilian and Urquhart, Lachlan: “In this paper we give a basic introduction to the transition in contemporary surveillance from top down traditional police surveillance to profiling and “pre-crime” methods. We then review in more detail the rise of open source (OSINT) and social media (SOCMINT) intelligence and its use by law enforcement and security authorities. Following this we consider what if any privacy protection is currently given in UK law to SOCMINT. Given the largely negative response to the above question, we analyse what reasonable expectations of privacy there may be for users of public social media, with reference to existing case law on art 8 of the ECHR. Two factors are in particular argued to be supportive of a reasonable expectation of privacy in open public social media communications: first, the failure of many social network users to perceive the environment where they communicate as “public”; and secondly, the impact of search engines (and other automated analytics) on traditional conceptions of structured dossiers as most problematic for state surveillance. Lastly, we conclude that existing law does not provide adequate protection foropen SOCMINT and that this will be increasingly significant as more and more personal data is disclosed and collected in public without well-defined expectations of privacy….(More)”

Big Data Before the Web

Curated on December 14, 2015August 3, 2018 by Stefaan Verhulst

Evan Hepler-Smith in the Wall Street Journal: “Sometime in the early 1950s, on a reservation in Wisconsin, a Menominee Indian man looked at an ink blot. An anthropologist recorded the man’s reaction according to a standard Rorschach-test protocol. The researcher submitted a copy of these notes to an enormous cache of records collected over the course of decades by American social scientists working among various “societies ‘other than our own.’ ” This entire collection of social-scientific data was photographed and printed in arrays of microscopic images on 3-by-5-inch cards. Sets of these cards were shipped to research libraries around the world. They gathered dust.

In the results of this Rorschach test, the anthropologist saw evidence of a culture eroded by modernity. Sixty years later, these documents also testify to the aspirations and fate of the social-scientific project for which they were generated. Deep within this forgotten Ozymandian card file sits the Menominee man’s reaction to Rorschach card VI: “It is like a dead planet. It seems to tell the story of a people once great who have lost . . . like something happened. All that’s left is the symbol.”

In “Database of Dreams: The Lost Quest to Catalog Humanity,” Rebecca Lemov delves into the ambitious efforts of mid-20th-century social scientists to build a “capacious and reliable science of the varieties of the human being” by generating an archive of human experience through interviews and tests and by storing the information on the high-tech media of the day.

For these psychologists and anthropologists, the key to a universal human science lay in studying members of cultures in transition between traditional and modern ways of life and in rendering their individuality as data. Interweaving stories of social scientists, Native American research subjects and information technologies, Ms. Lemov presents a compelling account of “what ‘humanness’ came to mean in an age of rapid change in technological and social conditions.” Ms. Lemov, an associate professor of the history of science at Harvard University, follows two contrasting threads through a story that she calls “a parable for our time.” She shows, first, how collecting data about human experience shapes human experience and, second, how a high-tech data repository of the 1950s became, as she puts it, a “data ruin.”…(More) – See also: Database of Dreams: The Lost Quest to Catalog Humanity

Data Science ethics

Curated on December 13, 2015August 3, 2018 by Stefaan Verhulst

Gov.uk blog: “If Tesco knows day-to-day how poorly the nation is, how can Government access similar insights so it can better plan health services? If Airbnb can give you a tailored service depending on your tastes, how can Government provide people with the right support to help them back into work in a way that is right for them? If companies are routinely using social media data to get feedback from their customers to improve their services, how can Government also use publicly available data to do the same?

Data science allows us to use new types of data and powerful tools to analyse this more quickly and more objectively than any human could. It can put us in the vanguard of policymaking – revealing new insights that leads to better and more tailored interventions. And it can help reduce costs, freeing up resource to spend on more serious cases.

But some of these data uses and machine-learning techniques are new and still relatively untested in Government. Of course, we operate within legal frameworks such as the Data Protection Act and Intellectual Property law. These are flexible but don’t always talk explicitly about the new challenges data science throws up. For example, how are you to explain the decision making process of a deep learning black box algorithm? And if you were able to, how would you do so in plain English and not a row of 0s and 1s?

We want data scientists to feel confident to innovate with data, alongside the policy makers and operational staff who make daily decisions on the data that the analysts provide –. That’s why we are creating an ethical framework which brings together the relevant parts of the law and ethical considerations into a simple document that helps Government officials decide what it can do and what it should do. We have a moral responsibility to maximise the use of data – which is never more apparent than after incidents of abuse or crime are left undetected – as well as to pay heed to the potential risks of these new tools. The guidelines are draft and not formal government policy, but we want to share them more widely in order to help iterate and improve them further….

So what’s in the framework? There is more detail in the fuller document, but it is based around six key principles:

Start with a clear user need and public benefit: this will help you justify the level of data sensitivity and method you use
Use the minimum level of data necessary to fulfill the public benefit: there are many techniques for doing so, such as de-identification, aggregation or querying against data
Build robust data science models: the model is only as good as the data it contains and while machines are less biased than humans they can get it wrong. It’s critical to be clear about the confidence of the model and think through unintended consequences and biases contained within the data
Be alert to public perceptions: put simply, what would a normal person on the street think about the project?
Be as open and accountable as possible: Transparency is the antiseptic for unethical behavior. Aim to be as open as possible (with explanations in plain English), although in certain public protection cases the ability to be transparent will be constrained.
Keep data safe and secure: this is not restricted to data science projects but we know that the public are most concerned about losing control of their data….(More)”