Sean D. Young in TechCrunch: “Although social media and online communities might have been developed for people to connect and share information, recent research shows that these technologies are really helpful in changing behaviors. My colleagues and I in the medical school, for instance, created online communities designed to improve health by getting people to do things, such as test for HIV, stop using methamphetamines, and just de-stress and relax. We don’t handpick people to join because we think they’ll love the technology; that’s not how science works. We invite them because the technology is relevant to them — they’re engaging in drugs, sex and other behaviors that might put themselves and others at risk. It’s our job to create the communities in a way that engages them enough to want to stay and participate. Yes, we do offer to pay them $30 to complete an hour-long survey, but then they are free to collect their money and never talk to us again. But for some reason, they stay in the group and decide to be actively engaged with strangers.
So how do we create online communities that keep people engaged and change their behaviors? Our starting point is to understand and address their psychological needs….
Throughout our research, we find that newly created online communities can change people’s behaviors by addressing the following psychological needs:
The Need to Trust. Sharing our thoughts, experiences, and difficulties with others makes us feel closer to others and increases our trust. When we trust people, we’re more open-minded, more willing to learn, and more willing to change our behavior. In our studies, we found that sharing personal information (even something as small as describing what you did today) can help increase trust and change behavior.
The Need to Fit In. Most of us inherently strive to fit in. Social norms, or other people’s attitudes and behaviors, heavily influence our own attitudes and behaviors. Each time a new online community or group forms, it creates its own set of social norms and expectations for how people should behave. Most people are willing to change their attitudes and/or behavior to fit these group norms and fit in with the community.
The Need for Self-Worth. When people feel good about themselves, they are more open to change and feel empowered to be able to change their behavior. When an online community is designed to have people support and care for each other, they can help to increase self-esteem.
The Need to Be Rewarded for Good Behavior. Anyone who has trained a puppy knows that you can get him to keep sitting as long as you keep the treats flowing to reward him, but if you want to wean him off the treats and really train him then you’ll need to begin spacing out the treats to make them less predictable. Well, people aren’t that different from animals in that way and can be trained with reinforcements too. For example, “liking” people’s communications when they immediately join a network, and then progressively spacing out the time that their posts are liked (psychologists call this variable reinforcement) can be incorporated onto social network platforms to encourage them to keep posting content. Eventually, these behaviors become habits.
The Need to Feel Empowered. While increasing self-esteem makes people feel good about themselves, increasing empowerment helps them know they have the ability to change. Creating a sense of empowerment is one of the most powerful predictors of whether people will change their behavior. Belonging to a network of people who are changing their own behaviors, support our needs, and are confident in our changing our behavior empowers us and gives us the ability to change our behavior.”
Imagining Data Without Division
Thomas Lin in Quanta Magazine: “As science dives into an ocean of data, the demands of large-scale interdisciplinary collaborations are growing increasingly acute…Seven years ago, when David Schimel was asked to design an ambitious data project called the National Ecological Observatory Network, it was little more than a National Science Foundation grant. There was no formal organization, no employees, no detailed science plan. Emboldened by advances in remote sensing, data storage and computing power, NEON sought answers to the biggest question in ecology: How do global climate change, land use and biodiversity influence natural and managed ecosystems and the biosphere as a whole?…
For projects like NEON, interpreting the data is a complicated business. Early on, the team realized that its data, while mid-size compared with the largest physics and biology projects, would be big in complexity. “NEON’s contribution to big data is not in its volume,” said Steve Berukoff, the project’s assistant director for data products. “It’s in the heterogeneity and spatial and temporal distribution of data.”
Unlike the roughly 20 critical measurements in climate science or the vast but relatively structured data in particle physics, NEON will have more than 500 quantities to keep track of, from temperature, soil and water measurements to insect, bird, mammal and microbial samples to remote sensing and aerial imaging. Much of the data is highly unstructured and difficult to parse — for example, taxonomic names and behavioral observations, which are sometimes subject to debate and revision.
And, as daunting as the looming data crush appears from a technical perspective, some of the greatest challenges are wholly nontechnical. Many researchers say the big science projects and analytical tools of the future can succeed only with the right mix of science, statistics, computer science, pure mathematics and deft leadership. In the big data age of distributed computing — in which enormously complex tasks are divided across a network of computers — the question remains: How should distributed science be conducted across a network of researchers?
Part of the adjustment involves embracing “open science” practices, including open-source platforms and data analysis tools, data sharing and open access to scientific publications, said Chris Mattmann, 32, who helped develop a precursor to Hadoop, a popular open-source data analysis framework that is used by tech giants like Yahoo, Amazon and Apple and that NEON is exploring. Without developing shared tools to analyze big, messy data sets, Mattmann said, each new project or lab will squander precious time and resources reinventing the same tools. Likewise, sharing data and published results will obviate redundant research.
To this end, international representatives from the newly formed Research Data Alliance met this month in Washington to map out their plans for a global open data infrastructure.”
Using Participatory Crowdsourcing in South Africa to Create a Safer Living Environment
New Paper by Bhaveer Bhana, Stephen Flowerday, and Aharon Satt in the International Journal of Distributed Sensor Networks: “The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations (utilising humans as sensors) of the city surroundings can be used to improve decision making in terms of managing these resources. However, the data collected must be of a certain quality in order to ensure that effective and efficient decisions are made. This study is focused on the improvement of emergency and non-emergency services (city resources) through the use of participatory crowdsourcing (humans as sensors) as a data collection method (collect public safety data), utilising voice technology in the form of an interactive voice response (IVR) system.
The study illustrates how participatory crowdsourcing (specifically humans as sensors) can be used as a Smart City initiative focusing on public safety by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision making when selecting an optimal crowdsourcing initiative. Public safety data quality criteria were developed to assess and identify the problems affecting data quality.
This study is guided by design science methodology and applies three driving theories: the Data Information Knowledge Action Result (DIKAR) model, the characteristics of a Smart City, and a credible Data Quality Framework. Four critical success factors were developed to ensure high quality public safety data is collected through participatory crowdsourcing utilising voice technologies.”
The study illustrates how participatory crowdsourcing (specifically humans as sensors) can be used as a Smart City initiative focusing on public safety by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision making when selecting an optimal crowdsourcing initiative. Public safety data quality criteria were developed to assess and identify the problems affecting data quality.
This study is guided by design science methodology and applies three driving theories: the Data Information Knowledge Action Result (DIKAR) model, the characteristics of a Smart City, and a credible Data Quality Framework. Four critical success factors were developed to ensure high quality public safety data is collected through participatory crowdsourcing utilising voice technologies.”
Mobile phone data are a treasure-trove for development
Paul van der Boor and Amy Wesolowski in SciDevNet: “Each of us generates streams of digital information — a digital ‘exhaust trail’ that provides real-time information to guide decisions that affect our lives. For example, Google informs us about traffic by using both its ‘My Location’ feature on mobile phones and third-party databases to aggregate location data. BBVA, one of Spain’s largest banks, analyses transactions such as credit card payments as well as ATM withdrawals to find out when and where peak spending occurs.This type of data harvest is of great value. But, often, there is so much data that its owners lack the know-how to process it and fail to realise its potential value to policymakers.
Meanwhile, many countries, particularly in the developing world, have a dearth of information. In resource-poor nations, the public sector often lives in an analogue world where piles of paper impede operations and policymakers are hindered by uncertainty about their own strengths and capabilities.Nonetheless, mobile phones have quickly pervaded the lives of even the poorest: 75 per cent of the world’s 5.5 billion mobile subscriptions are in emerging markets. These people are also generating digital trails of anything from their movements to mobile phone top-up patterns. It may seem that putting this information to use would take vast analytical capacity. But using relatively simple methods, researchers can analyse existing mobile phone data, especially in poor countries, to improve decision-making.
Think of existing, available data as low-hanging fruit that we — two graduate students — could analyse in less than a month. This is not a test of data-scientist prowess, but more a way of saying that anyone could do it.
There are three areas that should be ‘low-hanging fruit’ in terms of their potential to dramatically improve decision-making in information-poor countries: coupling healthcare data with mobile phone data to predict disease outbreaks; using mobile phone money transactions and top-up data to assess economic growth; and predicting travel patterns after a natural disaster using historical movement patterns from mobile phone data to design robust response programmes.
Another possibility is using call-data records to analyse urban movement to identify traffic congestion points. Nationally, this can be used to prioritise infrastructure projects such as road expansion and bridge building.
The information that these analyses could provide would be lifesaving — not just informative or revenue-increasing, like much of this work currently performed in developed countries.
But some work of high social value is being done. For example, different teams of European and US researchers are trying to estimate the links between mobile phone use and regional economic development. They are using various techniques, such as merging night-time satellite imagery from NASA with mobile phone data to create behavioural fingerprints. They have found that this may be a cost-effective way to understand a country’s economic activity and, potentially, guide government spending.
Another example is given by researchers (including one of this article’s authors) who have analysed call-data records from subscribers in Kenya to understand malaria transmission within the country and design better strategies for its elimination. [1]
In this study, published in Science, the location data of the mobile phones of more than 14 million Kenyan subscribers was combined with national malaria prevalence data. After identifying the sources and sinks of malaria parasites and overlaying these with phone movements, analysis was used to identify likely transmission corridors. UK scientists later used similar methods to create different epidemic scenarios for the Côte d’Ivoire.”
Prizes and Productivity: How Winning the Fields Medal Affects Scientific Output
New NBER working paper by George J. Borjas and Kirk B. Doran: “Knowledge generation is key to economic growth, and scientific prizes are designed to encourage it. But how does winning a prestigious prize affect future output? We compare the productivity of Fields medalists (winners of the top mathematics prize) to that of similarly brilliant contenders. The two groups have similar publication rates until the award year, after which the winners’ productivity declines. The medalists begin to “play the field,” studying unfamiliar topics at the expense of writing papers. It appears that tournaments can have large post-prize effects on the effort allocation of knowledge producers.”
The Contours of Crowd Capability
New paper by Prashant Shukla and John Prpi: “The existence of dispersed knowledge has been a subject of inquiry for more than six decades. Despite the longevity of this rich research tradition, the “knowledge problem” has remained largely unresolved both in research and practice, and remains “the central theoretical problem of all social science”. However, in the 21st century, organizations are presented with opportunities through technology to potentially benefit from the dispersed knowledge problem to some extent. One such opportunity is represented by the recent emergence of a variety of crowd-engaging information systems (IS).
In this vein, Crowdsourcing is being widely studied in numerous contexts, and the knowledge generated from these IS phenomena is well-documented. At the same time, other organizations are leveraging dispersed knowledge by putting in place IS-applications such as Predication Markets to gather large sample-size forecasts from within and without the organization. Similarly, we are also observing many organizations using IS-tools such as “Wikis” to access the knowledge of dispersed populations within the boundaries of the organization. Further still, other organizations are applying gamification techniques to accumulate Citizen Science knowledge from the public at large through IS.
Among these seemingly disparate phenomena, a complex ecology of crowd- engaging IS has emerged, involving millions of people all around the world generating knowledge for organizations through IS. However, despite the obvious scale and reach of this emerging crowd-engagement paradigm, there are no examples of research (as far as we know), that systematically compares and contrasts a large variety of these existing crowd-engaging IS-tools in one work. Understanding this current state of affairs, we seek to address this significant research void by comparing and contrasting a number of the crowd-engaging forms of IS currently available for organizational use.
Among these seemingly disparate phenomena, a complex ecology of crowd- engaging IS has emerged, involving millions of people all around the world generating knowledge for organizations through IS. However, despite the obvious scale and reach of this emerging crowd-engagement paradigm, there are no examples of research (as far as we know), that systematically compares and contrasts a large variety of these existing crowd-engaging IS-tools in one work. Understanding this current state of affairs, we seek to address this significant research void by comparing and contrasting a number of the crowd-engaging forms of IS currently available for organizational use.
To achieve this goal, we employ the Theory of Crowd Capital as a lens to systematically structure our investigation of crowd-engaging IS. Employing this parsimonious lens, we first explain how Crowd Capital is generated through Crowd Capability in organizations. Taking this conceptual platform as a point of departure, in Section 3, we offer an array of examples of IS currently in use in modern practice to generate Crowd Capital. We compare and contrast these emerging IS techniques using the Crowd Capability construct, therein highlighting some important choices that organizations face when entering the crowd- engagement fray. This comparison, which we term “The Contours of Crowd Capability”, can be used by decision-makers and researchers alike, to differentiate among the many extant methods of Crowd Capital generation. At the same time, our comparison also illustrates some important differences to be found in the internal organizational processes that accompany each form of crowd-engaging IS. In section 4, we conclude with a discussion of the limitations of our work.”
Smarter Than You Think: How Technology is Changing Our Minds for the Better
New book by Clive Thompson: “It’s undeniable—technology is changing the way we think. But is it for the better? Amid a chorus of doomsayers, Clive Thompson delivers a resounding “yes.” The Internet age has produced a radical new style of human intelligence, worthy of both celebration and analysis. We learn more and retain it longer, write and think with global audiences, and even gain an ESP-like awareness of the world around us. Modern technology is making us smarter, better connected, and often deeper—both as individuals and as a society.
In Smarter Than You Think Thompson shows that every technological innovation—from the written word to the printing press to the telegraph—has provoked the very same anxieties that plague us today. We panic that life will never be the same, that our attentions are eroding, that culture is being trivialized. But as in the past, we adapt—learning to use the new and retaining what’s good of the old.”
From Networked Publics to Issue Publics: Reconsidering the Public/Private Distinction in Web Science
New paper by Andreas Birkbak: “As an increasing part of everyday life becomes connected with the web in many areas of the globe, the question of how the web mediates political processes becomes still more urgent. Several scholars have started to address this question by thinking about the web in terms of a public space. In this paper, we aim to make a twofold contribution towards the development of the concept of publics in web science. First, we propose that although the notion of publics raises a variety of issues, two major concerns continue to be user privacy and democratic citizenship on the web. Well-known arguments hold that the complex connectivity of the web puts user privacy at risk and enables the enclosure of public debate in virtual echo chambers. Our first argument is that these concerns are united by a set of assumptions coming from liberal political philosophy that are rarely made explicit. As a second contribution, this paper points towards an alternative way to think about publics by proposing a pragmatist reorientation of the public/private distinction in web science, away from seeing two spheres that needs to be kept separate, towards seeing the public and the private as something that is continuously connected. The theoretical argument is illustrated by reference to a recently published case study of Facebook groups, and future research agendas for the study of web-mediated publics are proposed.”
Frontiers in Massive Data Analysis
New report from the National Academy of Sciences: “Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data.
Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale–terabytes and petabytes–is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge–from computer science, statistics, machine learning, and application disciplines–that must be brought to bear to make useful inferences from massive data.”
Connecting Grassroots to Government for Disaster Management
New Report by the Commons Lab (Wilson Center): “The growing use of social media and other mass collaboration technologies is opening up new opportunities in disaster management efforts, but is also creating new challenges for policymakers looking to incorporate these tools into existing frameworks, according to our latest report.
The Commons Lab, part of the Wilson Center’s Science & Technology Innovation Program, hosted a September 2012 workshop bringing together emergency responders, crisis mappers, researchers, and software programmers to discuss issues surrounding the adoption of these new technologies.
We are now proud to unveil “Connecting Grassroots to Government for Disaster Management: Workshop Summary,” a report discussing the key findings, policy suggestions, and success stories that emerged during the workshop. The report’s release coincides with the tenth annual Disaster Preparedness Month, sponsored by the Federal Emergency Management Agency in the Department of Homeland Security to help educate the public about preparing for emergencies. The report can be downloaded here.”