Four things policy-makers need to know about social media data and real time analytics.


Ella McPherson at LSE’s Impact Blog: “I recently gave evidence to the House of Commons Science and Technology Select Committee. This was based on written evidence co-authored with my colleague, Anne Alexander, and submitted to their ongoing inquiry into social media data and real time analytics. Both Anne and I research the use of social media during contested times; Anne looks at its use by political activists and labour movement organisers in the Arab world, and I look at its use in human rights reporting. In both cases, the need to establish facticity is high, as is the potential for the deliberate or inadvertent falsification of information. Similarly to the case that Carruthers makes about war reporting, we believe that the political-economic, methodological, and ethical issues raised by media dynamics in the context of crisis are bellwethers for the dynamics in more peaceful and mundane contexts.

From our work we have learned four crucial lessons that policy-makers considering this issue should understand:

1.  Social media information is vulnerable to a variety of distortions – some typical of all information, and others more specific to the characteristics of social media communications….

2.  If social media information is used to establish events, it must be verified; while technology can hasten this process, it is unlikely to ever occur real time due to the subjective, human element of judgment required….

 

3.  Verifying social media information may require identifying its source, which has ethical implications related to informed consent and anonymisation….

4.  Another way to think about social media information is as what Hermida calls an ‘awareness system,’ which reduces the need to collect source identities; under this approach, researchers look at volume rather than veracity to recognise information of interest… (More)

How can we ensure that cities create opportunities for healthy urbanization?


Blog by Roy Ahn, Thomas F. Burke & Anita M. McGahan on their new book: “By the year 2100, 8 out of 10 people in the world will reside in cities – a major change in demographics compared to 100 years ago.

Urbanization has sweeping consequences for population health. Most analysts evaluate the “specter of urbanization” by focusing on problems and challenges, which can include slum development, insecurity, and inequality.

As the World Health Organization and UN Habitat note in their seminal report, Hidden Cities, “Cities concentrate opportunities, jobs and services, but they also concentrate risks and hazards for health.” The urban poor are especially vulnerable because their housing conditions and access to clean water, sanitation, and health care are often severely compromised.

Additionally, the jobs available to the urban poor are often informal, dangerous, and temporary. Yet the lack of integrated governance and infrastructure responsible for urbanization problems also can create remarkable and often untapped opportunities for improving health. How can we ensure that cities create opportunities for healthy urbanization?

In our new book, Innovating for Healthy Urbanization, we argue that using the “innovations” lens can provide a unique platform through which solutions for urbanization and health can emerge.

Sometimes “innovations” can be decidedly high tech, such as holograms on medication packaging that protect against drug counterfeiters, or tiny filter paper tests costing pennies that exponentially increase access to medical diagnostic testing for poor people living in cities.

Other innovations are less tech-focused, but equally impactful, such as advocating for motorcycle helmet laws in cities or a low-cost, condom catheter-balloon kit that can save mothers from dying from postpartum hemorrhage.

What makes both high- and low-tech solutions effective? Pushing the envelope on what works and then integrating solutions to meet a community’s priority needs…..(More)”

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


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:

  1. Twitter is a popular platform in terms of the media attention it receives and it therefore attracts more research due to its cultural status
  2. Twitter makes it easier to find and follow conversations (i.e., by both its search feature and by tweets appearing in Google search results)
  3. Twitter has hashtag norms which make it easier gathering, sorting, and expanding searches when collecting data
  4. Twitter data is easy to retrieve as major incidents, news stories and events on Twitter are tend to be centred around a hashtag
  5. 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.
  6. 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:

  1. 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?
  2. 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?
  3. 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?
  4. 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)”

Democratising the Data Revolution


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)”

India PM releases ‘official Narendra Modi app’


David Reid at The Telegraph: “Narendra Modi, the Indian prime minister, who is already the third most popular world leader on Twitter, has extended his reach on social media by launching his own mobile app.

The app gives users regular updates on Mr Modi’s movements, and includes blog posts, interviews and “messages from the PM”….

Users can listen live to the Indian prime minister’s regular radio show, Mann Ki Baat and read about Mr Modi’s rise from “humble beginnings” on the biography section.

Another article explains why Mr Modi “opposes move to include his life story in school syllabus”.

A loyalty scheme rewards supporters with points and badges for filling out questionnaires and listening to Mr Modi’s speeches.

Mr Modi, who has 13 million followers on Twitter, is not the first politician to launch a personal app, although they are usually reserved for campaigning.

As well as Twitter, Mr Modi also has Facebook, Pinterest and YouTube accounts and his own website….(More)

Defining Public Engagement: A four-level approach.


Della Rucker’s Chapter 2 for an Online Public Engagement Book: “….public engagement typically means presenting information on an project or draft plan and addressing questions or comments. For planners working on long-range issues, such as a comprehensive plan, typical public engagement actions may include feedback questions, such as “what should this area look like?” or “what is your vision for the future of the neighborhood?” Such questions, while inviting participants to take a more active role in the community decision-making than the largely passive viewer/commenter in the first example, still places the resident in a peripheral role: that of an information source, functionally similar to the demographic data and GIS map layers that the professionals use to develop plans.

In a relatively small number of cases, planners and community advocates have found more robust and more direct means of engaging residents in decision -making around the future of their communities. Public engagement specialists, often originating from a community development or academic background, have developed a variety of methods, such as World Cafe and the Fishbowl, that are designed to facilitate more meaningful sharing of information among community residents, often as much with the intent of building connectivity and mutual understanding among residents of different backgrounds as for the purpose of making policy decisions.

Finally, a small but growing number of strategies have begun to emerge that place the work of making community decisions directly in the hands of private residents. Participatory -based budgeting allocates the decision about how to use a portion of a community’s budget to a citizen — based process, and participants work collaboratively through a process that determines what projects or initiatives will be funded in then coming budget cycle. And in the collection of tactics generally known as tactical urbanism or [other names], residents directly intervene in the physical appearance or function of the community by building and placing street furniture, changing parking spaces or driving lanes to pedestrian use, creating and installing new signs, or making other kinds of physical, typically temporary, changes — sometimes with, and sometimes without, the approval of the local government. The purposes of tactical urbanist interventions are twofold: they physically demonstrate the potential impact that more permanent features would have on the community’s transportation and quality of life, and they give residents a concrete and immediate opportunity to impact their environs.

The direct impacts of either participatory budgeting or tactical urbanism intiatives tend to be limited — the amount of budget available for a participatory-based budgeting initiative is usually a fraction of the total budget, and the physical area impacted by a tactical urbanism event is generally limited to a few blocks. Anecdotal evidence from both types of activity, however, seems to indicate an increased understanding of community needs and an increased sense of agency -of having the power to influence one’s community’s future — among participants.

Online public engagement methods have the potential to facilitate a wide variety of public engagement, from making detailed project information more readily available to enabling crowdsourced decision-making around budget and policy choices. However, any discussion of online public engagement methods will soon run up against the same basic challenge: when we use that term, what kind of engagement — what kind of participant experience — are we talking about?

We could divide public participation tasks according to one of several existing organization systems, or taxonomies. The two most commonly used in public engagement theory and practice derive from Sherry R. Arnestein’s 1969 academic paper, “A Ladder of Citizen Participation,” and the International Association of Public Participation’s Public Participation Spectrum.

Although these two taxonomies reflect the same basic idea — that one’s options in selecting public engagement activities range along a spectrum from generally less to more active engagement on the part of the public — they divide and label the classifications differently. …From my perspective, both of these frameworks capture the central issue of recognizing more to less intensive public engagement options, but the number of divisions and the sometimes abstract wording appears to have made it difficult for these insights to find widespread use outside of an academic context. Practitioners who need to think though these options seem to have some tendency to become tangled in the fine-grained differentiations, and the terminology can both make these distinctions harder to think about and lead to mistaken assumption that one is doing higher-level engagement that is actually the case. Among commercial online public engagement platform providers, blog posts claiming that their tool addresses the whole Spectrum appear on a relatively regular basis, even when the tool in questions is designed for feedback, not decision -making.

For these reasons, this book will use the following framework of engagement types, which is detailed enough to demarcate what I think are the most crucial differentiations while at the same time keeping the framework simple enough to use in routine process planning.

The four engagement types we will talk about are: Telling; Asking; Discussing; Deciding…(More)”

Handbook: How to Catalyze Humanitarian Innovation in Computing Research Institutes


Patrick Meier: “The handbook below provides practical collaboration guidelines for both humanitarian organizations & computing research institutes on how to catalyze humanitarian innovation through successful partnerships. These actionable guidelines are directly applicable now and draw on extensive interviews with leading humanitarian groups and CRI’s including the International Committee of the Red Cross (ICRC), United Nations Office for the Coordination of Humanitarian Affairs (OCHA), United Nations Children’s Fund (UNICEF), United Nations High Commissioner for Refugees (UNHCR), UN Global Pulse, Carnegie Melon University (CMU), International Business Machines (IBM), Microsoft Research, Data Science for Social Good Program at the University of Chicago and others.

This handbook, which is the first of its kind, also draws directly on years of experience and lessons learned from the Qatar Computing Research Institute’s (QCRI) active collaboration and unique partnerships with multiple international humanitarian organizations. The aim of this blog post is to actively solicit feedback on this first, complete working draft, which is available here as an open and editable Google Doc. …(More)”

How Crowdsourcing Can Help Us Fight ISIS


 at the Huffington Post: “There’s no question that ISIS is gaining ground. …So how else can we fight ISIS? By crowdsourcing data – i.e. asking a relevant group of people for their input via text or the Internet on specific ISIS-related issues. In fact, ISIS has been using crowdsourcing to enhance its operations since last year in two significant ways. Why shouldn’t we?

First, ISIS is using its crowd of supporters in Syria, Iraq and elsewhere to help strategize new policies. Last December, the extremist group leveraged its global crowd via social media to brainstorm ideas on how to kill 26-year-old Jordanian coalition fighter pilot Moaz al-Kasasba. ISIS supporters used the hashtag “Suggest a Way to Kill the Jordanian Pilot Pig” and “We All Want to Slaughter Moaz” to make their disturbing suggestions, which included decapitation, running al-Kasasba over with a bulldozer and burning him alive (which was the winner). Yes, this sounds absurd and was partly a publicity stunt to boost ISIS’ image. But the underlying strategy to crowdsource new strategies makes complete sense for ISIS as it continues to evolve – which is what the US government should consider as well.

In fact, in February, the US government tried to crowdsource more counterterrorism strategies. Via its official blog, DipNote, the State Departmentasked the crowd – in this case, US citizens – for their suggestions for solutions to fight violent extremism. This inclusive approach to policymaking was obviously important for strengthening democracy, with more than 180 entries posted over two months from citizens across the US. But did this crowdsourcing exercise actually improve US strategy against ISIS? Not really. What might help is if the US government asked a crowd of experts across varied disciplines and industries about counterterrorism strategies specifically against ISIS, also giving these experts the opportunity to critique each other’s suggestions to reach one optimal strategy. This additional, collaborative, competitive and interdisciplinary expert insight can only help President Obama and his national security team to enhance their anti-ISIS strategy.

Second, ISIS has been using its crowd of supporters to collect intelligence information to better execute its strategies. Since last August, the extremist group has crowdsourced data via a Twitter campaign specifically on Saudi Arabia’s intelligence officials, including names and other personal details. This apparently helped ISIS in its two suicide bombing attacks during prayers at a Shite mosque last month; it also presumably helped ISIS infiltrate a Saudi Arabian border town via Iraq in January. This additional, collaborative approach to intelligence collection can only help President Obama and his national security team to enhance their anti-ISIS strategy.

In fact, last year, the FBI used crowdsourcing to spot individuals who might be travelling abroad to join terrorist groups. But what if we asked the crowd of US citizens and residents to give us information specifically on where they’ve seen individuals get lured by ISIS in the country, as well as on specific recruitment strategies they may have noted? This might also lead to more real-time data points on ISIS defectors returning to the US – who are they, why did they defect and what can they tell us about their experience in Syria or Iraq? Overall, crowdsourcing such data (if verifiable) would quickly create a clearer picture of trends in recruitment and defectors across the country, which can only help the US enhance its anti-ISIS strategies.

This collaborative approach to data collection could also be used in Syria and Iraq with texts and online contributions from locals helping us to map ISIS’ movements….(More)”

Field experimenting in economics: Lessons learned for public policy


Robert Metcalfe at OUP Blog: “Do neighbourhoods matter to outcomes? Which classroom interventions improve educational attainment? How should we raise money to provide important and valued public goods? Do energy prices affect energy demand? How can we motivate people to become healthier, greener, and more cooperative? These are some of the most challenging questions policy-makers face. Academics have been trying to understand and uncover these important relationships for decades.

Many of the empirical tools available to economists to answer these questions do not allow causal relationships to be detected. Field experiments represent a relatively new methodological approach capable of measuring the causal links between variables. By overlaying carefully designed experimental treatments on real people performing tasks common to their daily lives, economists are able to answer interesting and policy-relevant questions that were previously intractable. Manipulation of market environments allows these economists to uncover the hidden motivations behind economic behaviour more generally. A central tenet of field experiments in the policy world is that governments should understand the actual behavioural responses of their citizens to changes in policies or interventions.

Field experiments represent a departure from laboratory experiments. Traditionally, laboratory experiments create experimental settings with tight control over the decision environment of undergraduate students. While these studies also allow researchers to make causal statements, policy-makers are often concerned subjects in these experiments may behave differently in settings where they know they are being observed or when they are permitted to sort out of the market.

For example, you might expect a college student to contribute more to charity when she is scrutinized in a professor’s lab than when she can avoid the ask altogether. Field experiments allow researchers to make these causal statements in a setting that is more generalizable to the behaviour policy-makers are directly interested in.

To date, policy-makers traditionally gather relevant information and data by using focus groups, qualitative evidence, or observational data without a way to identify causal mechanisms. It is quite easy to elicit people’s intentions about how they behave with respect to a new policy or intervention, but there is increasing evidence that people’s intentions are a poor guide to predicting their behaviour.

However, we are starting to see a small change in how governments seek to answer pertinent questions. For instance, the UK tax office (Her Majesty’s Revenue and Customs) now uses field experiments across some of its services to improve the efficacy of scarce taxpayers money. In the US, there are movements toward gathering more evidence from field experiments.

In the corporate world, experimenting is not new. Many of the current large online companies—such as Amazon, Facebook, Google, and Microsoft—are constantly using field experiments matched with big data to improve their products and deliver better services to their customers. More and more companies will use field experiments over time to help them better set prices, tailor advertising, provide a better customer journey to increase welfare, and employ more productive workers…(More).

See also Field Experiments in the Developed World: An Introduction (Oxford Review of Economic Policy)

Five Headlines from a Big Month for the Data Revolution


Sarah T. Lucas at Post2015.org: “If the history of the data revolution were written today, it would include three major dates. May 2013, when theHigh Level Panel on the Post-2015 Development Agenda first coined the phrase “data revolution.” November 2014, when the UN Secretary-General’s Independent Expert Advisory Group (IEAG) set a vision for it. And April 2015, when five headliner stories pushed the data revolution from great idea to a concrete roadmap for action.

The April 2015 Data Revolution Headlines

1. The African Data Consensus puts Africa in the lead on bringing the data revolution to the regional level. TheAfrica Data Consensus (ADC) envisions “a profound shift in the way that data is harnessed to impact on development decision-making, with a particular emphasis on building a culture of usage.” The ADC finds consensus across 15 “data communities”—ranging from open data to official statistics to geospatial data, and is endorsed by Africa’s ministers of finance. The ADC gets top billing in my book, as the first contribution that truly reflects a large diversity of voices and creates a political hook for action. (Stay tuned for a blog from my colleague Rachel Quint on the ADC).

2. The Sustainable Development Solutions Network (SDSN) gets our minds (and wallets) around the data needed to measure the SDGs. The SDSN Needs Assessment for SDG Monitoring and Statistical Capacity Development maps the investments needed to improve official statistics. My favorite parts are the clear typology of data (see pg. 12), and that the authors are very open about the methods, assumptions, and leaps of faith they had to take in the costing exercise. They also start an important discussion about how advances in information and communications technology, satellite imagery, and other new technologies have the potential to expand coverage, increase analytic capacity, and reduce the cost of data systems.

3. The Overseas Development Institute (ODI) calls on us to find the “missing millions.” ODI’s The Data Revolution: Finding the Missing Millions presents the stark reality of data gaps and what they mean for understanding and addressing development challenges. The authors highlight that even that most fundamental of measures—of poverty levels—could be understated by as much as a quarter. And that’s just the beginning. The report also pushes us to think beyond the costs of data, and focus on how much good data can save. With examples of data lowering the cost of doing government business, the authors remind us to think about data as an investment with real economic and social returns.

4. Paris21 offers a roadmap for putting national statistic offices (NSOs) at the heart of the data revolution.Paris21’s Roadmap for a Country-Led Data Revolution does not mince words. It calls on the data revolution to “turn a vicious cycle of [NSO] underperformance and inadequate resources into a virtuous one where increased demand leads to improved performance and an increase in resources and capacity.” It makes the case for why NSOs are central and need more support, while also pushing them to modernize, innovate, and open up. The roadmap gets my vote for best design. This ain’t your grandfather’s statistics report!

5. The Cartagena Data Festival features real-live data heroes and fosters new partnerships. The Festival featured data innovators (such as terra-i using satellite data to track deforestation), NSOs on the leading edge of modernization and reform (such as Colombia and the Philippines), traditional actors using old data in new ways (such as the Inter-American Development Bank’s fantastic energy database), groups focused on citizen-generated data (such as The Data Shift and UN My World), private firms working with big data for social good (such asTelefónica), and many others—all reminding us that the data revolution is well underway and will not be stopped. Most importantly, it brought these actors together in one place. You could see the sparks flying as folks learned from each other and hatched plans together. The Festival gets my vote for best conference of a lifetime, with the perfect blend of substantive sessions, intense debate, learning, inspiration, new connections, and a lot of fun. (Stay tuned for a post from my colleague Kristen Stelljes and me for more on Cartagena).

This month full of headlines leaves no room for doubt—momentum is building fast on the data revolution. And just in time.

With the Financing for Development (FFD) conference in Addis Ababa in July, the agreement of Sustainable Development Goals in New York in September, and the Climate Summit in Paris in December, this is a big political year for global development. Data revolutionaries must seize this moment to push past vision, past roadmaps, to actual action and results…..(More)”