Ebola’s Information Paradox

Curated on Posted on by Stefaan Verhulst
 Steven Johnson at The New York Times:” …The story of the Broad Street outbreak is perhaps the most famous case study in public health and epidemiology, in large part because it led to the revolutionary insight that cholera was a waterborne disease, not airborne as most believed at the time. But there is another element of the Broad Street outbreak that warrants attention today, as popular anxiety about Ebola surges across the airwaves and subways and living rooms of the United States: not the spread of the disease itself, but the spread of information about the disease.

It was a full seven days after Baby Lewis became ill, and four days after the Soho residents began dying in mass numbers, before the outbreak warranted the slightest mention in the London papers, a few short lines indicating that seven people had died in the neighborhood. (The report understated the growing death toll by an order of magnitude.) It took two entire weeks before the press began treating the outbreak as a major news event for the city.

Within Soho, the information channels were equally unreliable. Rumors spread throughout the neighborhood that the entire city had succumbed at the same casualty rate, and that London was facing a catastrophe on the scale of the Great Fire of 1666. But this proved to be nothing more than rumor. Because the Soho crisis had originated with a single-point source — the poisoned well — its range was limited compared with its intensity. If you lived near the Broad Street well, you were in grave danger. If you didn’t, you were likely to be unaffected.

Compare this pattern of information flow to the way news spreads now. On Thursday, Craig Spencer, a New York doctor, was given a diagnosis of Ebola after presenting a high fever, and the entire world learned of the test result within hours of the patient himself learning it. News spread with similar velocity several weeks ago with the Dallas Ebola victim, Thomas Duncan. In a sense, it took news of the cholera outbreak a week to travel the 20 blocks from Soho to Fleet Street in 1854; today, the news travels at nearly the speed of light, as data traverses fiber-optic cables. Thanks to that technology, the news channels have been on permanent Ebola watch for weeks now, despite the fact that, as the joke went on Twitter, more Americans have been married to Kim Kardashian than have died in the United States from Ebola.

As societies and technologies evolve, the velocities vary with which disease and information can spread. The tremendous population density of London in the 19th century enabled the cholera bacterium to spread through a neighborhood with terrifying speed, while the information about that terror moved more slowly. This was good news for the mental well-being of England’s wider population, which was spared the anxiety of following the death count as if it were a stock ticker. But it was terrible from a public health standpoint; the epidemic had largely faded before the official institutions of public health even realized the magnitude of the outbreak….

Information travels faster than viruses do now. This is why we are afraid. But this is also why we are safe.”

The Power of Data Analytics to Transform Government

Curated on Posted on by Stefaan Verhulst

Hugo Moreno at Forbes: “It’s mind boggling to consider the amount of information governments collect on their citizens. We often just expect them to manage and analyze it in a way that will benefit the general public and facilitate government transparency. However, it can be difficult to organize, manage and extract insights from these large, diverse data sets. According to “Analytics Paves the Way for Better Government,” a Forbes Insights case study sponsored by SAP, government leaders have called for investment in Big Data analytics capabilities to modernize government services and aid their economies. State and federal governments have begun to recognize the benefits of applying analytics. In fact, McKinsey & Co. estimates that by digitizing information, disseminating public data sets and applying analytics to improve decision making, governments around the world can act as catalysts for more than $3 trillion in economic value.
Governor Mike Pence of Indiana understands the importance of data and is keeping it at the center of his long-term vision for improving the management and effectiveness of government programs and making Indiana a leader in data-driven decision making. A year after taking office, he ordered state agencies to collaborate and share data to improve services. Data sharing is not a common practice in states, but the governor recognized that sharing data will lead to a successful enterprise.
Insights from analytics will help Indiana pursue six public policy goals: Increase private sector employment; attract new investment to the state; improve the quality of the state’s workforce; improve the health, safety and well-being of families; increase high school graduation rates; and improve the math and reading skills of elementary students….”

Proof: How Crowdsourced Election Monitoring Makes a Difference

Curated on Posted on by Stefaan Verhulst

Patrick Meier at iRevolution: “My colleagues Catie Bailard & Steven Livingston have just published the results of their empirical study on the impact of citizen-based crowdsourced election monitoring. Readers of iRevolution may recall that my doctoral dissertation analyzed the use of crowdsourcing in repressive environments and specifically during contested elections. This explains my keen interest in the results of my colleagues’ news data-driven study, which suggests that crowdsourcing does have a measurable and positive impact on voter turnout.

Reclaim Naija

Catie and Steven are “interested in digitally enabled collective action initiatives” spearheaded by “nonstate actors, especially in places where the state is incapable of meeting the expectations of democratic governance.” They are particularly interested in measuring the impact of said initiatives. “By leveraging the efficiencies found in small, incremental, digitally enabled contributions (an SMS text, phone call, email or tweet) to a public good (a more transparent election process), crowdsourced elections monitoring constitutes [an] important example of digitally-enabled collective action.” To be sure, “the successful deployment of a crowdsourced elections monitoring initiative can generate information about a specific political process—information that would otherwise be impossible to generate in nations and geographic spaces with limited organizational and administrative capacity.”

To this end, their new study tests for the effects of citizen-based crowdsourced election monitoring efforts on the 2011 Nigerian presidential elections. More specifically, they analyzed close to 30,000 citizen-generated reports of failures, abuses and successes which were publicly crowdsourced and mapped as part of the Reclaim Naija project. Controlling for a number of factors, Catie and Steven find that the number and nature of crowdsourced reports is “significantly correlated with increased voter turnout.”

In conclusion, the authors argue that “digital technologies fundamentally change information environments and, by doing so, alter the opportunities and constraints that the political actors face.” This new study is an important contribution to the literature and should be required reading for anyone interested in digitally-enabled, crowdsourced collective action. Of course, the analysis focuses on “just” one case study, which means that the effects identified in Nigeria may not occur in other crowdsourced, election monitoring efforts. But that’s another reason why this study is important—it will no doubt catalyze future research to determine just how generalizable these initial findings are.”

Creating a national citizen engagement process for energy policy

Curated on Posted on by Stefaan Verhulst

Paper by Nick Pidgeon et al in the Proceedings of the National Academy of Sciences (PNAS): “This paper examines some of the science communication challenges involved when designing and conducting public deliberation processes on issues of national importance. We take as our illustrative case study a recent research project investigating public values and attitudes toward future energy system change for the United Kingdom. National-level issues such as this are often particularly difficult to engage the public with because of their inherent complexity, derived from multiple interconnected elements and policy frames, extended scales of analysis, and different manifestations of uncertainty. With reference to the energy system project, we discuss ways of meeting a series of science communication challenges arising when engaging the public with national topics, including the need to articulate systems thinking and problem scale, to provide balanced information and policy framings in ways that open up spaces for reflection and deliberation, and the need for varied methods of facilitation and data synthesis that permit access to participants’ broader values. Although resource intensive, national-level deliberation is possible and can produce useful insights both for participants and for science policy.”

For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights

Curated on Posted on by Stefaan Verhulst
in the New York Times: “Technology revolutions come in measured, sometimes foot-dragging steps. The lab science and marketing enthusiasm tend to underestimate the bottlenecks to progress that must be overcome with hard work and practical engineering.

The field known as “big data” offers a contemporary case study. The catchphrase stands for the modern abundance of digital data from many sources — the web, sensors, smartphones and corporate databases — that can be mined with clever software for discoveries and insights. Its promise is smarter, data-driven decision-making in every field. That is why data scientist is the economy’s hot new job.

Yet far too much handcrafted work — what data scientists call “data wrangling,” “data munging” and “data janitor work” — is still required. Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets….”

Crowd-Sourced Augmented Realities: Social Media and the Power of Digital Representation

Curated on Posted on by Stefaan Verhulst

Pre-publication version of a chapter by Matthew Zook, Mark Graham and  Andrew Boulton  in S. Mains, J. Cupples, and C. Lukinbeal. Mediated Geographies/Geographies of Media. Springer Science International Handbooks in Human Geography, (Forthcoming): “A key and distinguishing feature of society today is that its increasingly documented by crowd-sourced social media discourse about public experiences. Much of this social media content is geo-referenced and exists in layers of information draped over the physical world, invisible to the naked eye but accessible to range of digital (and often) mobile devices. When we access these information layers, they mediate the mundane practices of everyday life, (e.g., What or who is nearby? How do I move from point A to B) through the creation of augmented realities, i.e., unstable, context dependent representations of places brought temporary into being by combining the space of material and virtual experience.
These augmented realities, as particular representations of locations, places and events, are vigorously promoted or contested and thus become important spots in which power is exercised, much in the same way that maps have long had power to reinforce or challenge the status quo. However, because many of the processes and practices behind the creation of augmented realities are unseen, its power is often overlooked in the process of representation or place-making. This paper highlights the points at which power acts and demonstrate that all representations of place – including augmented realities derived from social media – are products of and productive of, social relationships and associated power relations.”
Building upon a case study of Abbottabad, Pakistan after the raid on Osama bin Laden’s compound we construct a four-part typology of the power relations emerging from social practices that enact augmented realities. These include: Distributed power, the complex and socially/spatially distributed authorship of user-generated geospatial content; Communication power, the ways in which particular representations gain prominence; language is a particularly key variable; Code power, the autonomy of software code to regulate actions, or mediate content, or ordering representations in particular ways; and Timeless power, the ways in which digital representations of place reconfigure temporal relationships, particularly sequence and duration, between people and events.

Crowdsourcing Parking Lot Occupancy using a Mobile Phone Application

Curated on Posted on by Stefaan Verhulst

Paper by Davami, Erfan; Sukthankar, Gita available at ASE@360: “Participatory sensing is a specialized form of crowdsourcing for mobile devices in which the users act as sensors to report on local environmental conditions. • This poster describes the process of prototyping a mobile phone crowdsourcing app for monitoring parking availability on a large university campus. • We present a case study of how an agent-based urban model can be used to perform a sensitivity analysis of the comparative susceptibility of different data fusion paradigms to potentially troublesome user behaviors: 1. Poor user enrollment, 2. Infrequent usage, 3. A preponderance of untrustworthy users.”

The Emerging Power of Big Data

Curated on Posted on by Stefaan Verhulst

New America Foundation Report on the Chicago experience of using big data: “Big data is transforming the commercial marketplace but it also has the potential to reshape government affairs and urban development.  In a new report from the Emerging Leaders Program at the Chicago Council of Global Affairs, Lincoln S. Ellis, a founding member of the World Economic Roundtable, and other authors from the Emerging Leaders Program, explore how big data can be used by mega-cities to meet the challenges they face in an age of resource constraints to improve the lives of their residents.
Using Chicago as a case study, the report examines how the explosion of data availability enables cities to do more with less—to improve government services, fund much needed transportation, provide better education, and guarantee public safety.  And do more with less is what many cities have had to do over the past five years because many cities have had to cut their budgets and reduce the number of public employees in the post-financial crisis economy.  It is also what they will need to continue to do in the future.
“Unfortunately, resource constraints are a consistent feature of the post-crisis global landscape,” argues Ellis.  “Happily, so too is the renaissance in productivity gains garnered by our ability to leverage technology and information to achieve our most important public purposes in a smarter and more efficient way.”
Click here to view the report as a PDF.”

Selected Readings on Crowdsourcing Tasks and Peer Production

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of crowdsourcing was originally published in 2014.

Technological advances are creating a new paradigm by which institutions and organizations are increasingly outsourcing tasks to an open community, allocating specific needs to a flexible, willing and dispersed workforce. “Microtasking” platforms like Amazon’s Mechanical Turk are a burgeoning source of income for individuals who contribute their time, skills and knowledge on a per-task basis. In parallel, citizen science projects – task-based initiatives in which citizens of any background can help contribute to scientific research – like Galaxy Zoo are demonstrating the ability of lay and expert citizens alike to make small, useful contributions to aid large, complex undertakings. As governing institutions seek to do more with less, looking to the success of citizen science and microtasking initiatives could provide a blueprint for engaging citizens to help accomplish difficult, time-consuming objectives at little cost. Moreover, the incredible success of peer-production projects – best exemplified by Wikipedia – instills optimism regarding the public’s willingness and ability to complete relatively small tasks that feed into a greater whole and benefit the public good. You can learn more about this new wave of “collective intelligence” by following the MIT Center for Collective Intelligence and their annual Collective Intelligence Conference.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Benkler, Yochai. The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press, 2006. http://bit.ly/1aaU7Yb.

  • In this book, Benkler “describes how patterns of information, knowledge, and cultural production are changing – and shows that the way information and knowledge are made available can either limit or enlarge the ways people can create and express themselves.”
  • In his discussion on Wikipedia – one of many paradigmatic examples of people collaborating without financial reward – he calls attention to the notable ongoing cooperation taking place among a diversity of individuals. He argues that, “The important point is that Wikipedia requires not only mechanical cooperation among people, but a commitment to a particular style of writing and describing concepts that is far from intuitive or natural to people. It requires self-discipline. It enforces the behavior it requires primarily through appeal to the common enterprise that the participants are engaged in…”

Brabham, Daren C. Using Crowdsourcing in Government. Collaborating Across Boundaries Series. IBM Center for The Business of Government, 2013. http://bit.ly/17gzBTA.

  • In this report, Brabham categorizes government crowdsourcing cases into a “four-part, problem-based typology, encouraging government leaders and public administrators to consider these open problem-solving techniques as a way to engage the public and tackle difficult policy and administrative tasks more effectively and efficiently using online communities.”
  • The proposed four-part typology describes the following types of crowdsourcing in government:
    • Knowledge Discovery and Management
    • Distributed Human Intelligence Tasking
    • Broadcast Search
    • Peer-Vetted Creative Production
  • In his discussion on Distributed Human Intelligence Tasking, Brabham argues that Amazon’s Mechanical Turk and other microtasking platforms could be useful in a number of governance scenarios, including:
    • Governments and scholars transcribing historical document scans
    • Public health departments translating health campaign materials into foreign languages to benefit constituents who do not speak the native language
    • Governments translating tax documents, school enrollment and immunization brochures, and other important materials into minority languages
    • Helping governments predict citizens’ behavior, “such as for predicting their use of public transit or other services or for predicting behaviors that could inform public health practitioners and environmental policy makers”

Boudreau, Kevin J., Patrick Gaule, Karim Lakhani, Christoph Reidl, Anita Williams Woolley. “From Crowds to Collaborators: Initiating Effort & Catalyzing Interactions Among Online Creative Workers.” Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 14-060. January 23, 2014. https://bit.ly/2QVmGUu.

  • In this working paper, the authors explore the “conditions necessary for eliciting effort from those affecting the quality of interdependent teamwork” and “consider the the role of incentives versus social processes in catalyzing collaboration.”
  • The paper’s findings are based on an experiment involving 260 individuals randomly assigned to 52 teams working toward solutions to a complex problem.
  • The authors determined the level of effort in such collaborative undertakings are sensitive to cash incentives. However, collaboration among teams was driven more by the active participation of teammates, rather than any monetary reward.

Franzoni, Chiara, and Henry Sauermann. “Crowd Science: The Organization of Scientific Research in Open Collaborative Projects.” Research Policy (August 14, 2013). http://bit.ly/HihFyj.

  • In this paper, the authors explore the concept of crowd science, which they define based on two important features: “participation in a project is open to a wide base of potential contributors, and intermediate inputs such as data or problem solving algorithms are made openly available.” The rationale for their study and conceptual framework is the “growing attention from the scientific community, but also policy makers, funding agencies and managers who seek to evaluate its potential benefits and challenges. Based on the experiences of early crowd science projects, the opportunities are considerable.”
  • Based on the study of a number of crowd science projects – including governance-related initiatives like Patients Like Me – the authors identify a number of potential benefits in the following categories:
    • Knowledge-related benefits
    • Benefits from open participation
    • Benefits from the open disclosure of intermediate inputs
    • Motivational benefits
  • The authors also identify a number of challenges:
    • Organizational challenges
    • Matching projects and people
    • Division of labor and integration of contributions
    • Project leadership
    • Motivational challenges
    • Sustaining contributor involvement
    • Supporting a broader set of motivations
    • Reconciling conflicting motivations

Kittur, Aniket, Ed H. Chi, and Bongwon Suh. “Crowdsourcing User Studies with Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 453–456. CHI ’08. New York, NY, USA: ACM, 2008. http://bit.ly/1a3Op48.

  • In this paper, the authors examine “[m]icro-task markets, such as Amazon’s Mechanical Turk, [which] offer a potential paradigm for engaging a large number of users for low time and monetary costs. [They] investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks.”
  • The authors conclude that in addition to providing a means for crowdsourcing small, clearly defined, often non-skill-intensive tasks, “Micro-task markets such as Amazon’s Mechanical Turk are promising platforms for conducting a variety of user study tasks, ranging from surveys to rapid prototyping to quantitative measures. Hundreds of users can be recruited for highly interactive tasks for marginal costs within a timeframe of days or even minutes. However, special care must be taken in the design of the task, especially for user measurements that are subjective or qualitative.”

Kittur, Aniket, Jeffrey V. Nickerson, Michael S. Bernstein, Elizabeth M. Gerber, Aaron Shaw, John Zimmerman, Matthew Lease, and John J. Horton. “The Future of Crowd Work.” In 16th ACM Conference on Computer Supported Cooperative Work (CSCW 2013), 2012. http://bit.ly/1c1GJD3.

  • In this paper, the authors discuss paid crowd work, which “offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale.” However, they caution that, “it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework.”
  • The authors argue that seven key challenges must be met to ensure that crowd work processes evolve and reach their full potential:
    • Designing workflows
    • Assigning tasks
    • Supporting hierarchical structure
    • Enabling real-time crowd work
    • Supporting synchronous collaboration
    • Controlling quality

Madison, Michael J. “Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo.” In Convening Cultural Commons, 2013. http://bit.ly/1ih9Xzm.

  • This paper explores a “case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place, Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis on the Internet. In the second place, Galaxy Zoo is a highly successful example of peer production, some times known as crowdsourcing…In the third place, is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies.”
  • Madison concludes that the success of Galaxy Zoo has not been the result of the “character of its information resources (scientific data) and rules regarding their usage,” but rather, the fact that the “community was guided from the outset by a vision of a specific organizational solution to a specific research problem in astronomy, initiated and governed, over time, by professional astronomers in collaboration with their expanding universe of volunteers.”

Malone, Thomas W., Robert Laubacher and Chrysanthos Dellarocas. “Harnessing Crowds: Mapping the Genome of Collective Intelligence.” MIT Sloan Research Paper. February 3, 2009. https://bit.ly/2SPjxTP.

  • In this article, the authors describe and map the phenomenon of collective intelligence – also referred to as “radical decentralization, crowd-sourcing, wisdom of crowds, peer production, and wikinomics – which they broadly define as “groups of individuals doing things collectively that seem intelligent.”
  • The article is derived from the authors’ work at MIT’s Center for Collective Intelligence, where they gathered nearly 250 examples of Web-enabled collective intelligence. To map the building blocks or “genes” of collective intelligence, the authors used two pairs of related questions:
    • Who is performing the task? Why are they doing it?
    • What is being accomplished? How is it being done?
  • The authors concede that much work remains to be done “to identify all the different genes for collective intelligence, the conditions under which these genes are useful, and the constraints governing how they can be combined,” but they believe that their framework provides a useful start and gives managers and other institutional decisionmakers looking to take advantage of collective intelligence activities the ability to “systematically consider many possible combinations of answers to questions about Who, Why, What, and How.”

Mulgan, Geoff. “True Collective Intelligence? A Sketch of a Possible New Field.” Philosophy & Technology 27, no. 1. March 2014. http://bit.ly/1p3YSdd.

  • In this paper, Mulgan explores the concept of a collective intelligence, a “much talked about but…very underdeveloped” field.
  • With a particular focus on health knowledge, Mulgan “sets out some of the potential theoretical building blocks, suggests an experimental and research agenda, shows how it could be analysed within an organisation or business sector and points to possible intellectual barriers to progress.”
  • He concludes that the “central message that comes from observing real intelligence is that intelligence has to be for something,” and that “turning this simple insight – the stuff of so many science fiction stories – into new theories, new technologies and new applications looks set to be one of the most exciting prospects of the next few years and may help give shape to a new discipline that helps us to be collectively intelligent about our own collective intelligence.”

Sauermann, Henry and Chiara Franzoni. “Participation Dynamics in Crowd-Based Knowledge Production: The Scope and Sustainability of Interest-Based Motivation.” SSRN Working Papers Series. November 28, 2013. http://bit.ly/1o6YB7f.

  • In this paper, Sauremann and Franzoni explore the issue of interest-based motivation in crowd-based knowledge production – in particular the use of the crowd science platform Zooniverse – by drawing on “research in psychology to discuss important static and dynamic features of interest and deriv[ing] a number of research questions.”
  • The authors find that interest-based motivation is often tied to a “particular object (e.g., task, project, topic)” not based on a “general trait of the person or a general characteristic of the object.” As such, they find that “most members of the installed base of users on the platform do not sign up for multiple projects, and most of those who try out a project do not return.”
  • They conclude that “interest can be a powerful motivator of individuals’ contributions to crowd-based knowledge production…However, both the scope and sustainability of this interest appear to be rather limited for the large majority of contributors…At the same time, some individuals show a strong and more enduring interest to participate both within and across projects, and these contributors are ultimately responsible for much of what crowd science projects are able to accomplish.”

Schmitt-Sands, Catherine E. and Richard J. Smith. “Prospects for Online Crowdsourcing of Social Science Research Tasks: A Case Study Using Amazon Mechanical Turk.” SSRN Working Papers Series. January 9, 2014. http://bit.ly/1ugaYja.

  • In this paper, the authors describe an experiment involving the nascent use of Amazon’s Mechanical Turk as a social science research tool. “While researchers have used crowdsourcing to find research subjects or classify texts, [they] used Mechanical Turk to conduct a policy scan of local government websites.”
  • Schmitt-Sands and Smith found that “crowdsourcing worked well for conducting an online policy program and scan.” The microtasked workers were helpful in screening out local governments that either did not have websites or did not have the types of policies and services for which the researchers were looking. However, “if the task is complicated such that it requires ongoing supervision, then crowdsourcing is not the best solution.”

Shirky, Clay. Here Comes Everybody: The Power of Organizing Without Organizations. New York: Penguin Press, 2008. https://bit.ly/2QysNif.

  • In this book, Shirky explores our current era in which, “For the first time in history, the tools for cooperating on a global scale are not solely in the hands of governments or institutions. The spread of the Internet and mobile phones are changing how people come together and get things done.”
  • Discussing Wikipedia’s “spontaneous division of labor,” Shirky argues that the process is like, “the process is more like creating a coral reef, the sum of millions of individual actions, than creating a car. And the key to creating those individual actions is to hand as much freedom as possible to the average user.”

Silvertown, Jonathan. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24, no. 9 (September 2009): 467–471. http://bit.ly/1iha6CR.

  • This article discusses the move from “Science for the people,” a slogan adopted by activists in the 1970s to “’Science by the people,’ which is “a more inclusive aim, and is becoming a distinctly 21st century phenomenon.”
  • Silvertown identifies three factors that are responsible for the explosion of activity in citizen science, each of which could be similarly related to the crowdsourcing of skills by governing institutions:
    • “First is the existence of easily available technical tools for disseminating information about products and gathering data from the public.
    • A second factor driving the growth of citizen science is the increasing realisation among professional scientists that the public represent a free source of labour, skills, computational power and even finance.
    • Third, citizen science is likely to benefit from the condition that research funders such as the National Science Foundation in the USA and the Natural Environment Research Council in the UK now impose upon every grantholder to undertake project-related science outreach. This is outreach as a form of public accountability.”

Szkuta, Katarzyna, Roberto Pizzicannella, David Osimo. “Collaborative approaches to public sector innovation: A scoping study.” Telecommunications Policy. 2014. http://bit.ly/1oBg9GY.

  • In this article, the authors explore cases where government collaboratively delivers online public services, with a focus on success factors and “incentives for services providers, citizens as users and public administration.”
  • The authors focus on six types of collaborative governance projects:
    • Services initiated by government built on government data;
    • Services initiated by government and making use of citizens’ data;
    • Services initiated by civil society built on open government data;
    • Collaborative e-government services; and
    • Services run by civil society and based on citizen data.
  • The cases explored “are all designed in the way that effectively harnesses the citizens’ potential. Services susceptible to collaboration are those that require computing efforts, i.e. many non-complicated tasks (e.g. citizen science projects – Zooniverse) or citizens’ free time in general (e.g. time banks). Those services also profit from unique citizens’ skills and their propensity to share their competencies.”

Data Mining Reddit Posts Reveals How to Ask For a Favor–And Get it

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: “There’s a secret to asking strangers for something and getting it. Now data scientists say they’ve discovered it by studying successful requests on the web

One of the more extraordinary phenomena on the internet is the rise of altruism and of websites designed to enable it. The Random Acts of Pizza section of the Reddit website is a good example.

People leave messages asking for pizza which others fulfil if they find the story compelling. As the site says: “because… who doesn’t like helping out a stranger? The purpose is to have fun, eat pizza and help each other out. Together, we aim to restore faith in humanity, one slice at a time.”

A request might go something like this: “It’s been a long time since my mother and I have had proper food. I’ve been struggling to find any kind of work so I can supplement my mom’s social security… A real pizza would certainly lift our spirits”. Anybody can then fulfil the order which is then marked on the site with a badge saying “got pizza’d”, often with notes of thanks.

That raises an interesting question. What kinds of requests are most successful in getting a response? Today, we get an answer thanks to the work of Tim Althoff at Stanford University and a couple of pals who lift the veil on the previously murky question of how to ask for a favour—and receive it.

…

They analysed how various features might be responsible for the success of a post, such as the politeness of the post; its sentiment, whether positive or negative for example; its length. The team also looked at the similarity of the requester to the benefactor; and also the status of the requester.

Finally, they examined whether the post contained evidence of need in the form of a narrative that described why the requester needed free pizza.

Althoff and co used a standard machine learning algorithm to comb through all the possible correlations in 70 per cent of the data, which they used for training. Having found various correlations, they tested to see whether this had predictive power in the remaining 30 per cent of the data. In other words, can their algorithm predict whether a previously unseen request will be successful or not?

It turns out that their algorithm makes a successful prediction about 70 per cent of the time. That’s far from perfect but much better than random guessing which is right only half the time.

So what kinds of factors are important? Narrative is a key part of many of the posts, so Althoff and co spent some time categorising the types of stories people use.

They divided the narratives into five types, those that mention: money; a job; being a student; family; and a final group that includes mentions of friends, being drunk, celebrating and so on, which Althoff and co call ‘craving’.

Of these, narratives about jobs, family and money increase the probability of success. Student narratives have no effect while craving narratives significantly reduce the chances of success. In other words, narratives that communicate a need are more successful than those that do not.

 “We find that clearly communicating need through the narrative is essential,” say Althoff and co. And evidence of reciprocation helps too.

(Given these narrative requirements, it is not surprising that longer requests tend to be more successful than short ones.)

So for example, the following request was successful because it clearly demonstrates both need and evidence of reciprocation.

“My gf and I have hit some hard times with her losing her job and then unemployment as well for being physically unable to perform her job due to various hand injuries as a server in a restaurant. She is currently petitioning to have unemployment reinstated due to medical reasons for being unable to perform her job, but until then things are really tight and ANYTHING would help us out right now.

I’ve been both a giver and receiver in RAOP before and would certainly return the favor again when I am able to reciprocate. It took everything we have to pay rent today and some food would go a long ways towards making our next couple of days go by much better with some food.”

By contrast, the ‘craving’ narrative below demonstrates neither and was not successful.

“My friend is coming in town for the weekend and my friends and i are so excited because we haven’t seen him since junior high. we are going to a high school football game then to the dollar theater after and it would be so nice if someone fed us before we embarked :)”

Althoff and co also say that the status of the requester is an important factor too. “We find that Reddit users with higher status overall (higher karma) or higher status within the subcommunity (previous posts) are significantly more likely to receive help,” they say.

But surprisingly, being polite does not help (except by offering thanks).

That’s interesting work. Until now, psychologists have never understood the factors that make requests successful, largely because it has always been difficult to separate the influence of the request from what is being requested.

The key here is that everybody making requests in this study wants the same thing—pizza. In one swoop, this makes the data significantly easier to tease apart.

An important line of future work will be in using his work to understand altruistic behaviour in other communities too…

Ref:  http://arxiv.org/abs/1405.3282 : How to Ask for a Favor: A Case Study on the Success of Altruistic Requests”