Tag Archives: Information

The Value of Timely Information During Disasters (Measured in Hours)

In the 2005 World Disaster Report (PDF), the International Federation of the Red Cross states unequivocally that access to information during disasters is equally important as access to food, water, shelter and medication. Of all these commodities, however, crisis information is the most perishable. In other words, the “sell-by” or “use-by” date of information for decision-making during crisis is very short. Put simply: information rots fast, especially in the field (assuming that information even exists in the first place). But how fast exactly as measured in hours and days?

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Enter this handy graph by FEMA, which is based on a large survey of emergency management professionals across the US. As you’ll note, there is a very clear cut-off at 72 hours post-disaster by which time the value of information for decision making purposes has depreciated by 60% to 85%. Even at 48 hours, information has lost 35% to 65% of its initial tactical value. Disaster responders don’t have the luxury of waiting around for actionable information to inform their decisions during the first 24-72 hours after a disaster. So obviously they’ll take those decisions whether or not timely data is available to guide said decisions.

In a way, the graph also serves as a “historical caricature” of the availability of crisis information over the past 25 years:

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During the early 1990s, when the web and mobile phones were still in their infancy, it often took weeks to collect detailed information on disaster damage and needs following major disasters. Towards the end of the 2000’s, thanks to the rapid growth in smartphones, social media and the increasing availability of satellite imagery plus improvements in humanitarian information management systems, the time it took to collect crisis information was shortened. One could say we crossed the 72-hour time barrier on January 12, 2010 when a devastating earthquake struck Haiti. Five years later, the Nepal earthquake in April 2015 may have seen a number of formal responders crossing the 48-hour threshold.

While these observations are at best the broad brushstrokes of a caricature, the continued need for timely information is very real, especially for tactical decision making in the field. This is why we need to shift further left in the FEMA graph. Of course, information that is older than 48 hours is still useful, particularly for decision-makers at headquarters who do not need to make tactical decisions.

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In fact, the real win would be to generate and access actionable information within the first 12- to 24-hour mark. By the end of the 24-hours, the value of information has “only” depreciated by 10% to 35%. So how do we get to the top left corner of the graph? How do we get to “Win”?

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By integrating new and existing sensors and combining these with automated analysis solutions. New sensors: like Planet Lab’s growing constellation of micro-satellites, which will eventually image the entire planet once every 24 hours at around 3-meter resolution. And new automated analysis solutions: powered by crowdsourcing and artificial intelligence (AI), and in particular deep learning techniques to process the Big Data generated by these “neo-sensors” in near real-time, including multimedia posted to social media sites and the Web in general.

And the need for baseline data is no less important for comparative analysis and change detection purposes. As a colleague of mine recently noted, the value of baseline information before a major disaster is at an all time high but then itself depreciates as well post-disaster.

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Of course, access to real-time information does not make a humanitarian organization a real-time response organization. There are always delays regard-less of how timely (or not) the information is (assuming it is even available). But the real first responders are the local communities. So the real win here would be to make make this real-time analysis directly available to local partners in disaster prone countries. They often have more of an immediate incentive to generate and consume timely, tactical information. I described this information flow as “crowdfeeding” years ago.

In sum, the democratization of crisis information is key (keeping in mind data-protection protocols). But said democratization isn’t enough. The know-how and technologies to generate and analyze crisis information during the first 12-24 hours must also be democratized. The local capacity to respond quickly and effectively must exist; otherwise timely, tactical information will just rot away.


I’d be very interested to hear from human rights practitioners to get their thoughts on how/when the above crisis information framework does, and does not, apply when applied to human rights monitoring.

A 10 Year Vision: Future Trends in Geospatial Information Management

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The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) recently published their second edition of Future Trends in Geospatial Information Management. I blogged about the first edition here. Below are some of the excerpts I found interesting or noteworthy. The report itself is a 50-page document (PDF 7.1Mb).

  • The integration of smart technologies and efficient governance models will increase and the mantra of ‘doing more for less’ is more relevant than ever before.
  • There is an increasing tendency to bring together data from multiple sources: official statistics, geospatial information, satellite data, big data and crowdsourced data among them.
  • New data sources and new data collection technologies must be carefully applied to avoid a bias that favors countries that are wealthier and with established data infrastructures. The use of innovative tools might also favor those who have greater means to access technology, thus widening the gap between the ‘data poor’ and the ‘data rich’.
  • The paradigm of geospatial information is changing; no longer is it used just for mapping and visualization, but also for integrating with other data sources, data analytics, modeling and policy-making.
  • Our ability to create data is still, on the whole, ahead of our ability to solve complex problems by using the data.  The need to address this problem will rely on the development of both Big Data technologies and techniques (that is technologies that enable the analysis of vast quantities of information within usable and practical timeframes) and artificial intelligence (AI) or machine learning technologies that will enable the data to be processed more efficiently.
  • In the future we may expect society to make increasing use of autonomous machines and robots, thanks to a combination of aging population, 
rapid technological advancement in unmanned autonomous systems and AI, and the pure volume of data being beyond a human’s ability to process it.
  • Developments in AI are beginning to transform the way machines interact with the world. Up to now machines have mainly carried out well-defined tasks such as robotic assembly, or data analysis using pre-defined criteria, but we are moving into an age where machine learning will allow machines to interact with their environment in more flexible and adaptive ways. This is a trend we expect to 
see major growth in over the next 5 to 10 years as the technologies–and understanding of the technologies–become more widely recognized.
  • Processes based on these principles, and the learning of geospatial concepts (locational accuracy, precision, proximity etc.), can be expected to improve the interpretation of aerial and satellite imagery, by improving the accuracy with which geospatial features can be identified.
  • Tools may run persistently on continuous streams of data, alerting interested parties to new discoveries and events.  Another branch of AI that has long been of interest has been the expert system, in which the knowledge and experience of human experts 
is taught to a machine.
  • The principle of collecting data once only at the highest resolution needed, and generalizing ‘on the fly’ as required, can become reality.  Developments of augmented and virtual reality will allow humans to interact with data in new ways.
  • The future of data will not be the conflation of multiple data sources into a single new dataset, rather there will be a growth in the number of datasets that are connected and provide models to be used across the world.
  • Efforts should be devoted to integrating involuntary sensors– mobile phones, RFID sensors and so
on–which aside from their primary purpose may produce information regarding previously difficult to collect information. This leads to more real-time information being generated.
  • Many developing nations have leapfrogged in areas such as mobile communications, but the lack of core processing power may inhibit some from taking advantage of the opportunities afforded by these technologies.
  • Disaggregating data at high levels down to small area geographies. This will increase the need to evaluate and adopt alternative statistical modeling techniques to ensure that statistics can be produced at the right geographic level, whilst still maintaining the quality to allow them to be reported against.
  • The information generated through use of social media and the use of everyday devices will further reveal patterns and the prediction of behaviour. This is not a new trend, but as the use of social media 
for providing real-time information and expanded functionality increases it offers new opportunities for location based services.
  • There seems to have been
 a breakthrough from 2D to 3D information, and
 this is becoming more prevalent.

 Software already exists to process this information, and to incorporate the time information to create 4D products and services. It 
is recognized that a growth area over the next five to ten years will be the use of 4D information in a wide variety of industries.
  • 
 The temporal element is crucial to a number of applications such as emergency service response, for simulations and analytics, and the tracking of moving objects. 
 4D is particularly relevant in the context of real-time information; this has been linked to virtual reality technologies.
  • Greater coverage, quality and resolution has been achieved by the availability of both low-cost and affordable satellite systems, and unmanned aerial vehicles (UAVs). This has increased both the speed of collection and acquisition in remote areas, but also reduced the cost barriers of entry.
  • UAVs can provide real-time information to decision-makers on the ground providing, for example, information for disaster manage-ment. They are
 an invaluable tool when additional information 
is needed to improve vital decision making capabilities and such use of UAVs will increase.
  • The licensing of data in an increasingly online world is proving to be very challenging. There is a growth in organisations adopting simple machine-readable licences, but these have not resolved the issues to data. Emerging technologies such as web services and the growth of big data solutions drawn from multiple sources will continue to create challenges for the licensing of data.
  • A wider issue is the training and education of a broader community of developers and users of location-enabled content. At the same time there is a need for more automated approaches to ensuring the non-geospatial professional community get the right data at the right time. 
Investment in formal training in the use of geospatial data and its implementation is still indispensable.
  • Both ‘open’ and ‘closed’ VGI 
data play an important and necessary part of the wider data ecosystem.

Quantifying Information Flow During Emergencies

I was particularly pleased to see this study appear in the top-tier journal, Nature. (Thanks to my colleague Sarah Vieweg for flagging). Earlier studies have shown that “human communications are both temporally & spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness.” In this new study, the authors analyze crisis events using country-wide mobile phone data. To this end, they also analyze the communication patterns of mobile phone users outside the affected area. So the question driving this study is this: how do the communication patterns of non-affected mobile phone users differ from those affected? Why ask this question? Understanding the communication patterns of mobile phone users outside the affected areas sheds light on how situational awareness spreads during disasters.

Nature graphs

The graphs above (click to enlarge) simply depict the change in call volume for three crisis events and one non-emergency event for the two types of mobile phone users. The set of users directly affected by a crisis is labeled G0 while users they contact during the emergency are labeled G1. Note that G1 users are not affected by the crisis. Since the study seeks to assess how G1 users change their communication patterns following a crisis, one logical question is this: do the call volume of G1 users increase like those of G0 users? The graphs above reveal that G1 and G0 users have instantaneous and corresponding spikes for crisis events. This is not the case for the non-emergency event.

“As the activity spikes for G0 users for emergency events are both temporally and spatially localized, the communication of G1 users becomes the most important means of spreading situational awareness.” To quantify the reach of situational awareness, the authors study the communication patterns of G1 users after they receive a call or SMS from the affected set of G0 users. They find 3 types of communication patterns for G1 users, as depicted below (click to enlarge).

Nature graphs 2

Pattern 1: G1 users call back G0 users (orange edges). Pattern 2: G1 users call forward to G2 users (purple edges). Pattern 3: G1 users call other G1 users (green edges). Which of these 3 patterns is most pronounced during a crisis? Pattern 1, call backs, constitute 25% of all G1 communication responses. Pattern 2, call forwards, constitutes 70% of communications. Pattern 3, calls between G1 users only represents 5% of all communications. This means that the spikes in call volumes shown in the above graphs is overwhelmingly driven by Patterns 1 and 2: call backs and call forwards.

The graphs below (click to enlarge) show call volumes by communication patterns 1 and 2. In these graphs, Pattern 1 is the orange line and Pattern 2 the dashed purple line. In all three crisis events, Pattern 1 (call backs) has clear volume spikes. “That is, G1 users prefer to interact back with G0 users rather than contacting with new users (G2), a phenomenon that limits the spreading of information.” In effect, Pattern 1 is a measure of reciprocal communications and indeed social capital, “representing correspondence and coordination calls between social neighbors.” In contrast, Pattern 2 measures the dissemination of the “dissemination of situational awareness, corresponding to information cascades that penetrate the underlying social network.”

Nature graphs 3

The histogram below shows average levels of reciprocal communication for the 4 events under study. These results clearly show a spike in reciprocal behavior for the three crisis events compared to the baseline. The opposite is true for the non-emergency event.Nature graphs 4

In sum, a crisis early warning system based on communication patterns should seek to monitor changes in the following two indicators: (1) Volume of Call Backs; and (2) Deviation of Call Backs from baseline. Given that access to mobile phone data is near-impossible for the vast majority of academics and humanitarian professionals, one question worth exploring is whether similar communication dynamics can be observed on social networks like Twitter and Facebook.

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Can Official Disaster Response Apps Compete with Twitter?

There are over half-a-billion Twitter users, with an average of 135,000 new users signing up on a daily basis (1). Can emergency management and disaster response organizations win over some Twitter users by convincing them to use their apps in addition to Twitter? For example, will FEMA’s smartphone app gain as much “market share”? The app’s new crowdsourcing feature, “Disaster Reporter,” allows users to submit geo-tagged disaster-related images, which are then added to a public crisis map. So the question is, will more images be captured via FEMA’s app or from Twitter users posting Instagram pictures?

fema_app

This question is perhaps poorly stated. While FEMA may not get millions of users to share disaster-related pictures via their app, it is absolutely critical for disaster response organizations to explicitly solicit crisis information from the crowd. See my blog post “Social Media for Emergency Management: Question of Supply and Demand” for more information on the importance demand-driven crowdsourcing. The advantage of soliciting crisis information from a smartphone app is that the sourced information is structured and thus easily machine readable. For example, the pictures taken with FEMA’s app are automatically geo-tagged, which means they can be automatically mapped if need be.

While many, many more picture may be posted on Twitter, these may be more difficult to map. The vast majority of tweets are not geo-tagged, which means more sophisticated computational solutions are necessary. Instagram pictures are geo-tagged, but this information is not publicly available. So smartphone apps are a good way to overcome these challenges. But we shouldn’t overlook the value of pictures shared on Twitter. Many can be geo-tagged, as demonstrated by the Digital Humanitarian Network’s efforts in response to Typhoon Pablo. More-over, about 40% of pictures shared on Twitter in the immediate aftermath of the Oklahoma Tornado had geographic data. In other words, while the FEMA app may have 10,000 users who submit a picture during a disaster, Twitter may have 100,000 users posting pictures. And while only 40% of the latter pictures may be geo-tagged, this would still mean 40,000 pictures compared to FEMA’s 10,000. Recall that over half-a-million Instagram pictures were posted during Hurricane Sandy alone.

The main point, however, is that FEMA could also solicit pictures via Twitter and ask eyewitnesses to simply geo-tag their tweets during disasters. They could also speak with Instagram and perhaps ask them to share geo-tag data for solicited images. These strategies would render tweets and pictures machine-readable and thus automatically mappable, just like the pictures coming from FEMA’s app. In sum, the key issue here is one of policy and the best solution is to leverage multiple platforms to crowdsource crisis information. The technical challenge is how to deal with the high volume of pictures shared in real-time across multiple platforms. This is where microtasking comes in and why MicroMappers is being developed. For tweets and images that do not contain automatically geo-tagged data, MicroMappers has a microtasking app specifically developed to crowd-source the manual tagging of images.

In sum, there are trade-offs. The good news is that we don’t have to choose one solution over the other; they are complementary. We can leverage both a dedicated smartphone app and very popular social media platforms like Twitter and Facebook to crowdsource the collection of crisis information. Either way, a demand-driven approach to soliciting relevant information will work best, both for smartphone apps and social media platforms.

Bio

 

Tweets, Crises and Behavioral Psychology: On Credibility and Information Sharing

How we feel about the content we read on Twitter influences whether we accept and share it—particularly during disasters. My colleague Yasuaki Sakamoto at the Stevens Institute of Technology (SIT) and his PhD students analyzed this dyna-mic more closely in this recent study entitled “Perspective Matters: Sharing of Crisis Information in Social Media”. Using a series behavioral psychology experiments, they examined “how individuals share information related to the 9.0 magnitude earthquake, which hit northeastern Japan on March 11th, 2011.” Their results indicate that individuals were more likely to share crisis infor-mation (1) when they imagined that they were close to the disaster center, (2) when they were thinking about themselves, and (3) when they experienced negative emotions as a result of reading the information.

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Yasu and team are particularly interested in “the effects of perspective taking – considering self or other – and location on individuals’ intention to pass on information in a Twitter-like environment.” In other words: does empathy influence information sharing (retweeting) during crises? Does thinking of others in need eliminate the individual differences in perception that arise when thinking of one’s self instead? The authors hypothesize that “individuals’ information sharing decision can be influenced by (1) their imagined proximity, being close to or distant from the disaster center, (2) the perspective that they take, thinking about self or other, and (3) how they feel about the information that they are exposed to in social media, positive, negative or neutral.”

To test these hypotheses, Yasu and company collected one year’s worth of tweets posted by two major news agencies and five individuals following the Japan Earthquake on March 11, 2012. They randomly sampled 100 media tweets and 100 tweets produced by individuals, resulting a combined sample of 200 tweets. Sampling from these two sources (media vs user-generated) enables Yasu and team to test whether people treat the resulting content differently. Next, they recruited 468 volunteers from Amazon’s Mechanical Turk and paid them a nominal fee for their participation in a series of three behavioral psychology experiments.

In the first experiment, the “control” condition, volunteers read through the list of tweets and simply rated the likelihood of sharing a given tweet. The second experiment asked volunteers to read through the list and imagine they were in Fukushima. They were then asked to document their feelings and rate whether they would pass along a given message. Experiment three introduced a hypo-thetical person John based in Fukushima and prompted users to describe how each tweet might make John feel and rate whether they would share the tweet.

empathy

The results of these experiments suggest that, “people are more likely to spread crisis information when they think about themselves in the disaster situation. During disasters, then, one recommendation we can give to citizens would be to think about others instead of self, and think about others who are not in the disaster center. Doing so might allow citizens to perceive the information in a different way, and reduce the likelihood of impulsively spreading any seemingly useful but false information.” Yasu and his students also found that “people are more likely to share information associated with negative feelings.” Since rumors tend to evoke negativity,” they spread more quickly. The authors entertain possible ways to manage this problem such as “surrounding negative messages with positive ones,” for example.

In conclusion, Yasu and his students consider the design principles that ought to be considered when designing social media systems to verify and counter rumors. “In practice, designers need to devote significant efforts to understanding the effects of perspective taking and location, as shown in the current work, and develop techniques to mitigate negative influences of unproved information in social media.”

Bio

For more on Yasu’s work, see:

  • Using Crowdsourcing to Counter False Rumos on Social Media During Crises [Link]

Using Rapportive for Source and Information Verification

I’ve been using Rapportive for several few weeks now and have found the tool rather useful for assessing the trustworthiness of a source. Rapportive is an extension for Gmail that allows you to automatically visualize an email sender’s complete profile information right inside your inbox.

So now, when receiving emails from strangers, I can immediately see their profile picture, short bio, twitter handle (including latest tweets), links to their Facebook page, Google+ account, LinkedIn profile, blog, SkypeID, recent pictures they’ve posted, etc. As explained in my blog posts on information forensics, this type of meta-data can be particularly useful when assessing the importance or credibility of a source. To be sure, having a source’s entire digital footprint on hand can be quite revealing (as marketers know full well). Moreover, this type of meta-data was exactly what the Standby Volunteer Task Force was manually looking for when they sought to verify the identify of volunteers during the Libya Crisis Map project with the UN last year.

Obviously, the use of Rapportive alone is not a silver bullet to fully determine the credibility of a source or the authenticity of a source’s identity. But it does add contextual information that can make a difference when seeking to better under-stand the reliability of an email. I’d be curious to know whether Rapportive will be available as a stand-alone platform in the future so it can be used outside of Gmail. A simple web-based search box that allows one to search by email address, twitter handle, etc., with the result being a structured profile of that individual’s entire digital footprint. Anyone know whether similar platforms already exist? They could serve as ideal plugins for platforms like CrisisTracker.

From Crowdsourcing Crisis Information to Crowdseeding Conflict Zones (Updated)

Friends Peter van der Windt and Gregory Asmolov are two of the sharpest minds I know when it comes to crowdsourcing crisis information and crisis response. So it was a real treat to catch up with them in Berlin this past weekend during the “ICTs in Limited Statehood” workshop. An edited book of the same title is due out next year and promises to be an absolute must-read for all interested in the impact of Information and Communication Technologies (ICTs) on politics, crises and development.

I blogged about Gregory’s presentation following last year’s workshop, so this year I’ll relay Peter’s talk on research design and methodology vis-a-vis the collection of security incidents in conflict environments using SMS. Peter and mentor Macartan Humphreys completed their Voix des Kivus project in the DRC last year, which ran for just over 16 months. During this time, they received 4,783 text messages on security incidents using the FrontlineSMS platform. These messages were triaged and rerouted to several NGOs in the Kivus as well as the UN Mission there, MONUSCO.

How did they collect this information in the first place? Well, they considered crowdsourcing but quickly realized this was the wrong methodology for their project, which was to assess the impact of a major conflict mitigation program in the region. (Relaying text messages to various actors on the ground was not initially part of the plan). They needed high-quality, reliable, timely, regular and representative conflict event-data for their monitoring and evaluation project. Crowdsourcing is obviously not always the most appropriate methodology for the collection of information—as explained in this blog post.

Peter explained the pro’s and con’s of using crowdsourcing by sharing the framework above. “Knowledge” refers to the fact that only those who have knowledge of a given crowdsourcing project will know that participating is even an option. “Means” denotes whether or not an individual has the ability to participate. One would typically need access to a mobile phone and enough credit to send text messages to Voix des Kivus. In the case of the DRC, the size of subset “D” (no knowledge / no means) would easily dwarf the number of individuals comprising subset “A” (knowledge / means). In Peter’s own words:

“Crowdseeding brings the population (the crowd) from only A (what you get with crowdsourcing) to A+B+C+D: because you give phones&credit and you go to and inform the phoneholds about the project. So the crowd increases from A to A+B+C+D. And then from A+B+C+D one takes a representative sample. So two important benefits. And then a third: the relationship with the phone holder: stronger incentive to tell the truth, and no bad people hacking into the system.”

In sum, Peter and Macartan devised the concept of “crowdseeding” to increase the crowd and render that subset a representative sample of the overall population. In addition, the crowdseeding methodology they developed genera-ted more reliable information than crowdsourcing would have and did so in a way that was safer and more sustainable.

Peter traveled to 18 villages across the Kivus and in each identified three representatives to serve as the eyes and years of the village. These representatives were selected in collaboration with the elders and always included a female representative. They were each given a mobile phone and received extensive training. A code book was also shared which codified different types of security incidents. That way, the reps simply had to type the number corresponding to a given incident (or several numbers if more than one incident had taken place). Anyone in the village could approach these reps with relevant information which would then be texted to Peter and Macartan.

The table above is the first page of the codebook. Note that the numerous security risks of doing this SMS reporting were discussed at length with each community before embarking on the selection of 3 village reps. Each community decided to voted to participate despite the risks. Interestingly, not a single village voted against launching the project. However, Peter and Macartan chose not to scale the project beyond 18 villages for fear that it would get the attention of the militias operating in the region.

A local field representative would travel to the villages every two weeks or so to individually review the text messages sent out by each representative and to verify whether these incidents had actually taken place by asking others in the village for confirmation. The fact that there were 3 representatives per village also made the triangulation of some text messages possible. Because the 18 villages were randomly selected as part the randomized control trial (RCT) for the monitoring and evaluation project, the text messages were relaying a representative sample of information.

But what was the incentive? Why did a total of 54 village representatives from 18 villages send thousands of text messages to Voix des Kivus over a year and a half? On the financial side, Peter and Macartan devised an automated way to reimburse the cost of each text message sent on a monthly basis and in addition provided an additional $1.5/month. The only ask they made of the reps was that each had to send at least one text message per week, even if that message had the code 00 which referred to “no security incident”.

The figure above depicts the number of text messages received throughout the project, which formally ended in January 2011. In Peter’s own words:

“We gave $20 at the end to say thanks but also to learn a particular thing. During the project we heard often: ‘How important is that weekly $1.5?’ ‘Would people still send messages if you only reimburse them for their sent messages (and stop giving them the weekly $1.5)?’ So at the end of the project […] we gave the phone holder $20 and told them: the project continues exactly the same, the only difference is we can no longer send you the $1.5. We will still reimburse you for the sent messages, we will still share the bulletins, etc. While some phone holders kept on sending textmessages, most stopped. In other words, the financial incentive of $1.5 (in the form of phonecredit) was important.”

Peter and Macartan have learned a lot during this project, and I urge colleagues interested in applying their project to get in touch with them–I’m happy to provide an email introduction. I wish Swisspeace’s Early Warning System (FAST) had adopted this methodology before running out of funding several years ago. But the leadership at the time was perhaps not forward thinking enough. I’m not sure whether the Conflict Early Warning and Response Network (CEWARN) in the Horn has fared any better vis-a-vis demonstrated impact or lack thereof.

To learn more about crowdsourcing as a methodology for information collection, I recommend the following three articles: