Category Archives: Humanitarian Technologies

Making All Voices Count Using SMS and Advanced Computing

Posted on August 26, 2013 | 2 comments

Local communities in Uganda send UNICEF some 10,000 text messages (SMS) every week. These messages reflect the voices of Ugandan youths who use UNICEF’s U-report SMS platform to share their views on a range of social issues. Some messages are responses to polls created by UNICEF while others are unsolicited reports of problems that youths witness in their communities. About 40% of text messages received by UNICEF require an SMS reply providing advice or an answer to a question while 7% of messages require immediate action. Over 220,000 young people in Uganda have enrolled in U-report, with 200 to 1,000 new users joining on daily basis. UNICEF doesn’t have months or the staff to manually analyze this high volume and velocity of incoming text messages. This is where advanced computing comes in.

IBM recently partnered with UNICEF Uganda to develop an automated system to classify incoming text messages. (If this sounds familiar to iRevolution readers it is because my team and I at QCRI are developing a similar platform called Artificial Intelligence for Disaster Response, or AIDR. While our system is first and foremost geared towards classifying tweets, it can also be used to filter large volumes of SMS). The automated platform classifies incoming text messages into one (or more) of the following categories: water, health & nutrition, orphans & vulnerable children, violence against children, education, employment, social policy, emergency, u-report, energy, family & relationships, irrelevant and poll.

IBM created machine learning classifiers that are 40% more accurate than a keyword based approach for automate classification. The predictive quality of the individual classifiers ranged from a low of 69.8% for family & relationships and a high of 98.4 for water-related issues. See full list of results in table above. Note that the IBM platform is limited to English-based text messages but the team is looking to provide multi-lingual support in the future.

UNICEF is using this system to automatically route classified tweets to the appropriate departments. For example, UNICEF recently received a surge of text messages about nodding disease and responded by sending out a series of mass SMS’s to communities living in the affected region. These text messages provided information on how to recognize symptoms and ways to get treated. The feedback loop also includes government agencies and ministries. Indeed, all Members of Parliament and Chief Administrative Officers receive SMS updates based on the automated classification platform.

U-report is now being deployed in Zambia, South Sudan, Yemen, Democratic Republic of Congo, Zimbabwe and Burundi. I plan to get in touch with the team at IBM to learn more about these deployments and explore where we at QCRI may be able to help given our related work on AIDR. In the meantime, many thanks to my colleague Claudia Perlich for pointing me to this project. To learn more about IBM’s automated system, please see this paper (PDF).

2 Comments

Posted in Big Data, Crowdsourcing, Humanitarian Technologies

Tagged Automated, IBM, SMS, U-report, Uganda

Can Official Disaster Response Apps Compete with Twitter?

Posted on August 13, 2013 | 6 comments

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?

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.

6 Comments

Posted in Big Data, Crowdsourcing, Humanitarian Technologies, Social Media

Tagged Apps, Disaster, FEMA, Information, Smartphone, Twitter

The First Ever Spam Filter for Disaster Response

Posted on July 29, 2013 | 11 comments

While spam filters provide additional layers of security to websites, they can also be used to process all kinds of information. Perhaps most famously, for example, the reCAPTCHA spam filter was used to transcribe the New York Times’ entire paper-based archives. See my previous blog post to learn how this was done and how spam filters can also be used to process information for disaster response. Given the positive response I received from humanitarian colleagues who read the blog post, I teamed up with my colleagues at QCRI to create the first ever spam filter for disaster response.

During international disasters, the humanitarian community (often lead by the UN’s Office for the Coordination of Humanitarian Affairs, OCHA) needs to carry out rapid damage assessments. Recently, these assessments have included the analysis of pictures shared on social media following a disaster. For example, OCHA activated the Digital Humanitarian Network (DHN) to collect and quickly tag pictures that capture evidence of damage in response to Typhoon Pablo in the Philippines (as described here and TEDx talk above). Some of these pictures, which were found on Twitter, were also geo-referenced by DHN volunteers. This enabled OCHA to create (over night) the unique damage assessment map below.

OCHA intends to activate the DHN again in future disasters to replicate this type of rapid damage assessment operation. This is where spam filters come in. The DHN often needs support to quickly tag these pictures (which may number in the tens of thousands). Adding a spam filter that requires email users to tag which image captures disaster damage not only helps OCHA and other organizations carry out a rapid damage assessment, but also increases the security of email systems at the same time. And it only takes 3 seconds to use the spam filter.

My team and I at QCRI have thus developed a spam filter plugin that can be easily added to email login pages like OCHA’s as shown above. When the Digital Humanitarian Network requires additional hands on deck to tag pictures during disasters, this plugin can simply be switched on. My team at QCRI can easily push the images to the plugin and pull data on which images have been tagged as showing disaster damage. The process for the end user couldn’t be simpler. Enter your username and password as normal and then simply select the picture below that shows disaster damage. If there are none, then simply click on “None” and then “Login”. The spam filter uses a predictive algorithm and an existing data-base of pictures as a control mechanism to ensure that the filter cannot be gamed. On that note, feel free to test the plugin here. We’d love your feedback as we continue testing.

The desired outcome? Each potential disaster picture is displayed to 3 different email account users. Only if each of the 3 users tag the same picture as capturing disaster damage does that picture get automatically forwarded to members of the Digital Humanitarian Network. To tag more pictures after logging in, users are invited to do so via MicroMappers, which launches this September in partnership with OCHA. MicroMappers enables members of the public to participate in digital disaster response efforts with a simple click of the mouse.

I would ideally like to see an innovative and forward-thinking organization like OCHA pilot the plugin for a two week feasibility test. If the results are positive and promising, then I hope OCHA and other UN agencies engaged in disaster response adopt the plugin more broadly. As mentioned in my previous blog post, the UN employs well over 40,000 people around the world. Even if “only” 10% login in one day, that’s still 4,000 images effortlessly tagged for use by OCHA and others during their disaster relief operations. Again, this plugin would only be used in response to major disasters when the most help is needed. We’ll be making the code for this plugin freely available and open source.

Please do get in touch if you’d like to invite your organization to participate in this innovative humanitarian technology project. You can support disaster response efforts around the world by simply logging into your email account, web portal, or Intranet!

11 Comments

Posted in Big Data, Crowdsourcing, Humanitarian Technologies

Tagged Disaster, Filter, OCHA, ReCAPTCHA, Response, Spam

Crowdsourcing Life-Saving Assistance

Posted on July 23, 2013 | 2 comments

Disaster responders cannot be everywhere at the same time, but the crowd is always there. The same is true for health care professionals such as critical care paramedics who work with an ambulance service. Paramedics cannot be posted everywhere. Can crowdsourcing help? This was the question posed to me by my colleague Mark who overseas the ambulance personnel for a major city.

Take Sudden Cardiac Arrest (SCA), for example. SCA’s account for an estimated 325,000 deaths each year in the US—one person every two minutes. Survival rates nationally are less than 8%. But Cardio-Pulmonary Resuscitation, or CPR, can sustain life until paramedics arrive by maintaining blood flow to the heart and brain. “Without oxygen-rich blood, permanent brain damage or death can occur in less than 8 minutes. After 10 minutes there is little chance of successful resuscitation. Even in modern urban settings the response times for professional rescuers commonly approach these time frames” (1). This explains why “effective bystander CPR, provided immediately after sudden cardiac arrest, can double or triple a person’s chance of survival” (2). In fact, close to 60% of adults in the US say they have taken CPR training (often due to school requirements) and 11% say they have used CPR in an actual emergency (3).

So why not develop a dedicated smartphone app to alert bystanders when someone nearby is suffering from a Sudden Cardiac Arrest? This is what Mark was getting at when we started this conversation back in April. Well it just so happens that such an app does exist. The PulsePoint mobile app “alerts CPR-trained bystanders to someone nearby having a sudden cardiac arrest that may require CPR. The app is activated by the local public safety communications center simultaneous with the dispatch of local fire and EMS resources” (4).

In sum, the purpose of the app is to increase survival rates by:

Reducing collapse-to-CPR times by increasing citizen awareness of cardiac events beyond a traditional “witnessed” area.

Reducing collapse-to-defibrillation times by increasing awareness of public access defibrillator (AED) locations through real-time mapping of nearby devices.

The PulsePoint approach is instructive to those of us applying technology to improve international humanitarian response. First, the app works within, not outside, existing institutions. When someone calls 911 to report a cardiac arrest, paramedics are still dispatched to the scene. At the same time, emergency operators use PulsePoint to alert registered bystanders in the area. Second, volunteers who receive an alert are provided with a map of nearby AEDs, i.e., additional “meta-data” important for rapid response. Third, training is key. Without CPR training, the “crowd” is not empowered to help. So Community Emergency Response Teams (CERTs) are important. Of course, not all needs require special expertise to be fulfilled, but preparedness still goes a long way.

2 Comments

Posted in Crowdsourcing, Humanitarian Technologies

Tagged Ambulance, App, CPR, Mobile, Paramedic, PulsePoint, SAC, Smartphone

Disaster Response Plugin for Online Games

Posted on July 17, 2013 | 1 comment

The Internet Response League (IRL) was recently launched for online gamers to participate in supporting disaster response operations. A quick introduction to IRL is available here. Humanitarian organizations are increasingly turning to online volunteers to filter through social media reports (e.g. tweets, Instagram photos) posted during disasters. Online gamers already spend millions of hours online every day and could easily volunteer some of their time to process crisis information without ever having to leave the games they’re playing.

Lets take World of Warcraft, for example. If a gamer has opted in to receive disaster alerts, they’d see screens like the one above when logging in or like the one below whilst playing a game.

If a gamer accepts the invitation to join the Internet Response League, they’d see the “Disaster Tagging” screen below. There they’d tag as many pictures as wish by clicking on the level of disaster damage they see in each photo. Naturally, gamers can exit the disaster tagging area at any time to return directly to their game.

Each picture would be tagged by at least 3 gamers in order to ensure the accuracy of the tagging. That is, if 3 volunteers tag the same image as “Severe”, then we can be reasonably assured that the picture does indeed show infrastructure damage. These pictures would then be sent back to IRL and shared with humanitarian organizations for rapid damage assessment analysis. There are already precedents for this type of disaster response tagging. Last year, the UN asked volunteers to tag images shared on Twitter after a devastating Typhoon hit the Philippines. More specifically, they asked them to tag images that captured the damage caused by the Typhoon. You can learn more about this humanitarian response operation here.

IRL is now looking to develop a disaster response plugin like the one described above. This way, gaming companies will have an easily embeddable plugin that they can insert into their gaming environments. For more on this plugin and the latest updates on IRL, please visit the IRL website here. We’re actively looking for feedback and welcome collaborators and partnerships.

Acknowledgements: Screenshots created by my colleague Peter Mosur who is the co-founder of the IRL.

1 Comment

Posted in Big Data, Humanitarian Technologies, Social Computing, Social Media

Tagged Disaster, Games, Gaming, microtasking, online, Response

Why the Share Economy is Important for Disaster Response and Resilience

Posted on July 15, 2013 | 14 comments

A unique and detailed survey funded by the Rockefeller Foundation confirms the important role that social and community bonds play vis-à-vis disaster resilience. The new study, which focuses on resilience and social capital in the wake of Hurricane Sandy, reveals how disaster-affected communities self-organized, “with reports of many people sharing access to power, food and water, and providing shelter.” This mutual aid was primarily coordinated face-to-face. This may not always be possible, however. So the “Share Economy” can also play an important role in coordinating self-help during disasters.

In a share economy, “asset owners use digital clearinghouses to capitalize the unused capacity of things they already have, and consumers rent from their peers rather than rent or buy from a company” (1). During disasters, these asset owners can use the same digital clearinghouses to offer what they have at no cost. For example, over 1,400 kindhearted New Yorkers offered free housing to people heavily affected by the hurricane. They did this using AirBnB, as shown in the short video above. Meanwhile, on the West Coast, the City of San Francisco has just lunched a partnership with BayShare, a sharing economy advocacy group in the Bay Area. The partnership’s goal is to “harness the power of sharing to ensure the best response to future disasters in San Francisco” (2).

While share economy platforms like AirBnB are still relatively new, many believe that “the share economy is a real trend and not some small blip (3). So it may be worth taking an inventory of share platforms out there that are likely to be useful for disaster response. Here’s a short list:

AirBnB: A global travel rental platform with accommodations in 192 countries. This service has already been used for disaster response as described above.

Fon: Enables people to share some of their home Wi-Fi in exchange for getting free Wi-Fi from 8 million people in Fon’s network. Access to information is always key during & after disasters. The map above displays a subset of all Fon users in that part of Europe.

LendingClub: A cheaper service than credit cards for borrowers. Also provides better interest rates than savings accounts for investors. Access to liquidity is often necessary after a disaster.

LiquidSpace: Provides high quality temporary workspaces and office rentals. These can be rented by the hour and by the day. Dedicated spaces are key for coordinating disaster response.

Lyft: An is on-demand ride-sharing smartphone app for cheaper, safer rides. This service could be used to transport people and supplies following a disaster. Similar to Sidecar.

RelayRides: A car sharing marketplace where participants can rent out their own cars. Like Lyft, RelayRides could be used to transport goods and people. Similar to Getaround. Also, ParkingPanda is the parking equivalent.

TaskRabbit: Get your deliveries and errands completed easily & quickly by trusted individuals in your neighborhood. This service could be used to run quick errands following disasters. Similar to Zaarly, a marketplace that helps you discover and hire local services.

Yerdle: An “eBay” for sharing items with your friends. This could be used to provide basic supplies to disaster-affected neighborhoods. Similar to SnapGood, which also allows for temporary sharing.

Feel free to add more examples via the comments section below if you know of other sharing economy platforms that could be helpful during disasters.

While these share tools don’t necessary reinforce bonding social capital since face-to-face interactions are not required, they do stand to increase levels of bridging social capital. The former refers to social capital within existing social networks while the latter refers to “cooperative connections with people from different walks of life,” and is often considered “more valuable than ‘bonding social capital'” (3). Bridging social capital is “closely related to thin trust, as opposed to the bonding social capital of thick trust” (4). Platforms that facilitate the sharing economy provide reassurance vis-à-vis the thin trust since they tend to vet participants. This extra reassurance can go a long way during disasters and may thus facilitate mutual-aid at a distance.

14 Comments

Posted in Crowdsourcing, Disaster Resilience, Humanitarian Technologies, Social Media

Tagged AirBnB, Disaster, Disaster Resilience, Economy, Share

Using Crowdring for Disaster Response?

Posted on June 18, 2013 | 2 comments

35 million missed calls.

That’s the number of calls that 75-year old social justice leader Anna Hazare received from people across India who supported his efforts to fight corruption. Two weeks earlier, he had invited India to join his movement by making “missed calls” to a local number. Missed calls, known as beeping or flashing, are calls that are intentionally dropped after ringing. The advantage of making missed call is that neither the caller or recipient is charged. This tactic is particularly common in emerging economies to avoid paying for air time or SMS. To build on this pioneering work, Anna and his team are developing a mobile petition tool called Crowdring, which turns a free “missed call” into a signature on a petition.

Communicating with disaster-affected communities is key for effective disaster response. Crowdring could be used to poll disaster affected communities. The service could also be used in combination with local community radio stations. The latter would broadcast a series of yes or no questions; ringing once would signify yes, twice would mean no. Some questions that come to mind:

Do you have enough drinking water?
Are humanitarian organizations doing a good job?
Is someone in your household displaying symptoms of cholera?

By receiving these calls, humanitarians would automatically be able to create a database of phone numbers with associated poll results. This means they could text them right back for more information or to arrange an in person meeting. You can learn more about Crowdring in this short video below.

2 Comments

Posted in Digital Activism, Humanitarian Technologies

Tagged Beeping, Crowdring, Disaster, Flashing, Mobile, Response

Using Waze, Uber, AirBnB and SeeClickFix for Disaster Response

Posted on June 11, 2013 | 6 comments

After the Category 5 Tornado in Oklahoma, map editors at Waze used the service to route drivers around the damage. While Uber increased their car service fares during Hurricane Sandy, they could have modified their App to encourage the shared use of Uber cars to fill unused seats. This would have taken some work, but AirBnB did modify their platform overnight to let over 1,400 kindhearted New Yorkers offer free housing to victims of the hurricane. SeeClick fix was used also to report over 800 issues in just 24 hours after Sandy made landfall. These included reports on the precise location of power outages, flooding, downed trees, downed electric lines, and other storm damage. Following the Boston Marathon Bombing, SeeClick fix was used to quickly find emergency housing for those affected by the tragedy.

Disaster-affected populations have always been the real first responders. Paid emergency response professionals cannot be everywhere at the same time, but the crowd is always there. Disasters are collective experiences; and today, disaster-affected crowds are increasingly “digital crowds” as well—that is, both a source and consumer of that digital information. In other words, they are also the first digital responders. Thanks to connection technologies like Waze, Uber, AirBnB and SeeClickFix, disaster affected communities can self-organize more quickly than ever before since these new technologies drastically reduce the cost and time necessary to self-organize. And because resilience is a function of a community’s ability to self-organize, these new technologies can also render disaster-prone populations more resilient by fostering social capital, thus enabling them to bounce back more quickly after a crisis.

When we’re affected by disasters, we tend to use the tools that we are most familiar with, i.e. those we use on a daily basis when there is no disaster. That’s why we often see so many Facebook updates, Instagram pictures, tweets, YouTube videos, etc., posted during a disaster. The same holds true for services like Waze and AirBnB, for example. So I’m thrilled to see more examples of these platforms used as humanitarian technologies and equally heartened to know that the companies behind these tools are starting to play a more active role during disasters, thus helping people help themselves. Each of these platforms have the potential to become hyper-local match.com’s for disaster response. Facilitating this kind of mutual-aid not only builds social capital, which is critical to resilience, it also shifts the burden and pressure off the shoulders of paid responders who are often overwhelmed during major disasters.

In sum, these useful everyday technologies also serve to crowdsource and democratize disaster response. Do you know of other examples? Other everyday smartphone apps and web-based apps that get used for disaster response? If so, I’d love to know. Feel free to post your examples in the comments section below. Thanks!

6 Comments

Posted in Crowdsourcing, Humanitarian Technologies, Social Media

Tagged AirBnB, Disaster, Response, SeeClickFix, Uber, Waze

Could CrowdOptic Be Used For Disaster Response?

Posted on June 5, 2013 | 5 comments

Crowds—rather than sole individuals—are increasingly bearing witness to disasters large and small. Instagram users, for example, snapped 800,000 #Sandy pictures during the hurricane last year. One way to make sense of this vast volume and velocity of multimedia content—Big Data—during disasters is with PhotoSynth, as blogged here. Another perhaps more sophisticated approach would be to use CrowdOptic, which automatically zeros in on the specific location that eyewitnesses are looking at when using their smartphones to take pictures or recording videos.

How does it work? CrowdOptic simply triangulates line-of-sight intersections using sensory metadata from pictures and videos taken using a smartphone. The basic approach is depicted in the figure below. The areas of intersection is called a focal cluster. CrowdOptic automatically identifies the location of these clusters.

“Once a crowd’s point of focus is determined, any content generated by that point of focus is automatically authenticated, and a relative significance is assigned based on CrowdOptic’s focal data attributes […].” These include: (1) Number of Viewers; (2) Location of Focus; (3) Distance to Epicenter; (4) Cluster Timestamp, Duration; and (5) Cluster Creation, Dissipation Speed.” CrowdOptic can also be used on live streams and archival images & videos. Once a cluster is identified, the best images/videos pointing to this cluster are automatically selected.

Clearly, all this could have important applications for disaster response and information forensics. My colleagues and I recently collected over 12,000 Instagram pictures and more than 5,000 YouTube videos posted to Twitter during the first 48 hours of the Tornado in Oklahoma. These could be uploaded to CrowdOptic for cluster identification. Any focal cluster with several viewers would almost certainly be authentic, particularly if the time-stamps are similar. These clusters could then be tagged by digital humanitarian volunteers based on whether they depict evidence of disaster damage. Indeed, we could have tested out CrowdOptic during in the disaster response efforts we carried out for the United Nations following the devastating Philippines Typhoon. Perhaps CrowdOptic could facilitate rapid damage assessments in the future. Of course, the value of CrowdOptic ultimately depends on the volume of geotagged images and videos shared on social media and the Web.

I once wrote a blog post entitled, “Wag the Dog, or How Falsifying Crowdsourced Data Can Be a Pain.” While an image or video could certainly be falsified, trying to fake several focal clusters of multimedia content with dozens of viewers each would probably require the equivalent organization capacity of a small movie-production or commercial. So I’m in touch with the CrowdOptic team to explore the possibility of carrying out a proof of concept based on the multimedia data we’ve collected following the Oklahoma Tornados. Stay tuned!

5 Comments

Posted in Big Data, Crowdsourcing, Humanitarian Technologies, Social Media

Tagged CrowdOptic, Disaster, Photosynth, Response

Data Mining Wikipedia in Real Time for Disaster Response

Posted on June 4, 2013 | 3 comments

My colleague Fernando Diaz has continued working on an interesting Wikipedia project since he first discussed the idea with me last year. Since Wikipedia is increasingly used to crowdsource live reports on breaking news such as sudden-onset humanitarian crisis and disasters, why not mine these pages for structured information relevant to humanitarian response professionals?

In computing-speak, Sequential Update Summarization is a task that generates useful, new and timely sentence-length updates about a developing event such as a disaster. In contrast, Value Tracking tracks the value of important event-related attributes such as fatalities and financial impact. Fernando and his colleagues will be using both approaches to mine and analyze Wikipedia pages in real time. Other attributes worth tracking include injuries, number of displaced individuals, infrastructure damage and perhaps disease outbreaks. Pictures of the disaster uploaded to a given Wikipedia page may also be of interest to humanitarians, along with meta-data such as the number of edits made to a page per minute or hour and the number of unique editors.

Fernando and his colleagues have recently launched this tech challenge to apply these two advanced computing techniques to disaster response based on crowdsourced Wikipedia articles. The challenge is part of the Text Retrieval Conference (TREC), which is being held in Maryland this November. As part of this applied research and prototyping challenge, Fernando et al. plan to use the resulting summarization and value tracking from Wikipedia to verify related crisis information shared on social media. Needless to say, I’m really excited about the potential. So Fernando and I are exploring ways to ensure that the results of this challenge are appropriately transferred to the humanitarian community. Stay tuned for updates.

See also: Web App Tracks Breaking News Using Wikipedia Edits [Link]

3 Comments

Posted in Big Data, Crowdsourcing, Humanitarian Technologies

Tagged Disaster, Response, Wikipedia

Category Archives: Humanitarian Technologies

Making All Voices Count Using SMS and Advanced Computing

The First Ever Spam Filter for Disaster Response

Crowdsourcing Life-Saving Assistance

Using Crowdring for Disaster Response?

Data Mining Wikipedia in Real Time for Disaster Response

Patrick Meier, PhD

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Category Archives: Humanitarian Technologies

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Patrick Meier, PhD

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