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


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

 

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


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)”

Transforming City Governments for Successful Smart Cities


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

White House to make public records more public


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

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

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

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

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

Democratising the Data Revolution


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)

The case for data ethics


Steven Tiell at Accenture: “Personal data is the coin of the digital realm, which for business leaders creates a critical dilemma. Companies are being asked to gather more types of data faster than ever to maintain a competitive edge in the digital marketplace; at the same time, however, they are being asked to provide pervasive and granular control mechanisms over the use of that data throughout the data supply chain.

The stakes couldn’t be higher. If organizations, or the platforms they use to deliver services, fail to secure personal data, they expose themselves to tremendous risk—from eroding brand value and the hard-won trust of established vendors and customers to ceding market share, from violating laws to costing top executives their jobs.

To distinguish their businesses in this marketplace, leaders should be asking themselves two questions. What are the appropriate standards and practices our company needs to have in place to govern the handling of data? And how can our company make strong data controls a value proposition for our employees, customers and partners?

Defining effective compliance activities to support legal and regulatory obligations can be a starting point. However, mere compliance with existing regulations—which are, for the most part, focused on privacy—is insufficient. Respect for privacy is a byproduct of high ethical standards, but it is only part of the picture. Companies need to embrace data ethics, an expansive set of practices and behaviors grounded in a moral framework for the betterment of a community (however defined).

 RAISING THE BAR

Why ethics? When communities of people—in this case, the business community at large—encounter new influences, the way they respond to and engage with those influences becomes the community’s shared ethics. Individuals who behave in accordance with these community norms are said to be moral, and those who are exemplary are able to gain the trust of their community.

Over time, as ethical standards within a community shift, the bar for trustworthiness is raised on the assumption that participants in civil society must, at a minimum, adhere to the rule of law. And thus, to maintain moral authority and a high degree of trust, actors in a community must constantly evolve to adopt the highest ethical standards.

Actors in the big data community, where security and privacy are at the core of relationships with stakeholders, must adhere to a high ethical standard to gain this trust. This requires them to go beyond privacy law and existing data control measures. It will also reward those who practice strong ethical behaviors and a high degree of transparency at every stage of the data supply chain. The most successful actors will become the platform-based trust authorities, and others will depend on these platforms for disclosure, sharing and analytics of big data assets.

Data ethics becomes a value proposition only once controls and capabilities are in place to granularly manage data assets at scale throughout the data supply chain. It is also beneficial when a community shares the same behavioral norms and taxonomy to describe the data itself, the ethical decision points along the data supply chain, and how those decisions lead to beneficial or harmful impacts….(More)”

Mining citizen emotions to estimate the urgency of urban issues


Christian Masdeval and Adriano Veloso in Information Systems: “Crowdsourcing technology offers exciting possibilities for local governments. Specifically, citizens are increasingly taking part in reporting and discussing issues related to their neighborhood and problems they encounter on a daily basis, such as overflowing trash-bins, broken footpaths and lifts, illegal graffiti, and potholes. Pervasive citizen participation enables local governments to respond more efficiently to these urban issues. This interaction between citizens and municipalities is largely promoted by civic engagement platforms, such as See-Click-Fix, FixMyStreet, CitySourced, and OpenIDEO, which allow citizens to report urban issues by entering free text describing what needs to be done, fixed or changed. In order to develop appropriate action plans and priorities, government officials need to figure out how urgent are the reported issues. In this paper we propose to estimate the urgency of urban issues by mining different emotions that are implicit in the text describing the issue. More specifically, a reported issue is first categorized according to the emotions expressed in it, and then the corresponding emotion scores are combined in order to produce a final urgency level for the reported issue. Our experiments use the SeeClickFix hackathon data and diverse emotion classification algorithms. They indicate that (i) emotions can be categorized efficiently with supervised learning algorithms, and (ii) the use of citizen emotions leads to accurate urgency estimates. Further, using additional features such as the type of issue or its author leads to no further accuracy gains….(More)”

Using social media in hotel crisis management: the case of bed bugs


Social media has helped to bridge the communication gap between customers and hotels. Bed bug infestations are a growing health crisis and have obtained increasing attention on social media sites. Without managing this crisis effectively, bed bug infestation can cause economic loss and reputational damages to hotel properties, ranging from negative comments and complaints, to possible law suits. Thus, it is essential for hoteliers to understand the importance of social media in crisis communication, and to incorporate social media in hotels’ crisis management plans.

This study serves as one of the first attempts in the hospitality field to offer discussions and recommendations on how hotels can manage the bed bug crisis and other crises of this kind by incorporating social media into their crisis management practices….(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)”