Jointly: Peer-to-Peer Disaster Recovery App

My colleague Samia Kallidis is launching a brilliant self-help app to facilitate community-based disaster recovery efforts. Samia is an MFA Candidate at the School of Visual Arts in New York. While her work on this peer-to-peer app began as part of her thesis, she has since been accepted to the NEA Studio Incubator Program to make her app a reality. NEA provides venture capital to help innovative entrepreneurs build transformational initiatives around the world. So huge congrats to Samia on this outstanding accomplishment. I was already hooked back in February when she presented her project at NYU and am even more excited now. Indeed, there are exciting synergies with the MatchApp project I’m working on with QCRI and MIT-CSAIL , which is why we’re happily exploring ways to collaborate & complement our respective initiatives.

Samia’s app is aptly called Jointly and carries the tag line: “More Recovery, Less Red Tape.” In her February presentation, Samia made many very compelling arguments for a self-help approach to disaster response based on her field research and interviews she conducted following Hurricane Sandy. She rightly noted that many needs that arise during the days, weeks and months following a disaster do not require the attention of expert disaster response professionals—in fact these responders may not have the necessary skills to match the needs that frequently arise after a disaster (assuming said responders even stay the course). Samia also remarked that solving little challenges and addressing the little needs that surface post-disaster can make the biggest differences. Hence Jointly. In her own words:

“Jointly is a decentralized mobile application that helps communities self-organize disaster relief without relying on bureaucratic organizations. By directly connecting disaster victims with volunteers, Jointly allows individuals to request help through services and donations, and to find skilled volunteers who are available to fulfill those needs. This minimizes waste of resources, reduces duplication of services, and significantly shortens recovery time for individuals and communities.”

Samia kindly shared the above video and screenshots of Jointly below.

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I’m thrilled to see Jointly move forward and am excited to be collaborating with Samia on the Jointly and MatchApp connection. We certainly share the same goal: to help people help themselves. Indeed, increasing this capacity for self-organization builds resilience. These connection technologies and apps provide for more rapid and efficient self-help actions in times of need. This doesn’t mean that professional disaster response organizations are obsolete—quite on the contrary, in fact. Organizations like the American Red Cross can feed relevant service delivery data to the apps so that affected communities also know where, when and how to access these. In Jointly, official resources will be geo-tagged and updated live in the “Resources” part of the app.

You can contact Samia directly at: hello@jointly.us should you be interested in learning more or collaborating with her.

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Web App Tracks Breaking News Using Wikipedia Edits

A colleague of mine at Google recently shared a new and very interesting Web App that tracks breaking news events by monitoring Wikipedia edits in real-time. The App, Wikipedia Live Monitor, alerts users to breaking news based on the frequency of edits to certain articles. Almost every significant news event has a Wikipedia page that gets updated in near real-time and thus acts as a single, powerful cluster for tacking an evolving crisis.

Wikipedia Live Monitor

Social media, in contrast, is far more distributed, which makes it more difficult to track. In addition, social media is highly prone to false positives. These, however, are almost immediately corrected on Wikipedia thanks to dedicated editors. Wikipedia Live Monitor currently works across several dozen languages and also “cross-checks edits with social media updates on Twitter, Google Plus and Facebook to help users get a better sense of what is trending” (1).

I’m really excited to explore the use of this Live Monitor for crisis response and possible integration with some of the humanitarian technology platforms that my colleagues and I at QCRI are developing. For example, the Monitor could be used to supplement crisis information collected via social media using the Artificial Intelligence for Disaster Response (AIDR) platform. In addition, the Wikipedia Monitor could also be used to triangulate reports posted to our Verily platform, which leverages time-critical crowdsourcing techniques to verify user-generated content posted on social media during disasters.

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Social Media for Emergency Management: Question of Supply and Demand

I’m always amazed by folks who dismiss the value of social media for emergency management based on the perception that said content is useless for disaster response. In that case, libraries are also useless (bar the few books you’re looking for, but those rarely represent more than 1% of all the books available in a major library). Does that mean libraries are useless? Of course not. Is social media useless for disaster response? Of course not. Even if only 0.001% of the 20+ million tweets posted during Hurricane Sandy were useful, and only half of these were accurate, this would still mean over 1,000 real-time and informative tweets, or some 15,000 words—i.e., the equivalent of a 25-page, single-space document exclusively composed of fully relevant, actionable & timely disaster information.

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Empirical studies clearly prove that social media reports can be informative for disaster response. Numerous case studies have also described how social media has saved lives during crises. That said, if emergency responders do not actively or explicitly create demand for relevant and high quality social media content during crises, then why should supply follow? If the 911 emergency number (999 in the UK) were never advertised, then would anyone call? If 911 were simply a voicemail inbox with no instructions, would callers know what type of actionable information to relay after the beep?

While the majority of emergency management centers do not create the demand for crowdsourced crisis information, members of the public are increasingly demanding that said responders monitor social media for “emergency posts”. But most responders fear that opening up social media as a crisis communication channel with the public will result in an unmanageable flood of requests, The London Fire Brigade seems to think otherwise, however. So lets carefully unpack the fear of information flooding.

First of all, New York City’s 911 operators receive over 10 million calls every year that are accidental, false or hoaxes. Does this mean we should abolish the 911 system? Of course not. Now, assuming that 10% of these calls takes an operator 10 seconds to manage, this represents close to 3,000 hours or 115 days worth of “wasted work”. But this filtering is absolutely critical and requires human intervention. In contrast, “emergency posts” published on social media can be automatically filtered and triaged thanks to Big Data Analytics and Social Computing, which could save time operators time. The Digital Operations Center at the American Red Cross is currently exploring this automated filtering approach. Moreover, just as it is illegal to report false emergency information to 911, there’s no reason why the same laws could not apply to social media when these communication channels are used for emergency purposes.

Second, if individuals prefer to share disaster related information and/or needs via social media, this means they are less likely to call in as well. In other words, double reporting is unlikely to occur and could also be discouraged and/or penalized. In other words, the volume of emergency reports from “the crowd” need not increase substantially after all. Those who use the phone to report an emergency today may in the future opt for social media instead. The only significant change here is the ease of reporting for the person in need. Again, the question is one of supply and demand. Even if relevant emergency posts were to increase without a comparable fall in calls, this would simply reveal that the current voice-based system creates a barrier to reporting that discriminates against certain users in need.

Third, not all emergency calls/posts require immediate response by a paid professional with 10+ years of experience. In other words, the various types of needs can be triaged and responded to accordingly. As part of their police training or internships, new cadets could be tasked to respond to less serious needs, leaving the more seasoned professionals to focus on the more difficult situations. While this approach certainly has some limitations in the context of 911, these same limitations are far less pronounced for disaster response efforts in which most needs are met locally by the affected communities themselves anyway. In fact, the Filipino government actively promotes the use of social media reporting and crisis hashtags to crowdsource disaster response.

In sum, if disaster responders and emergency management processionals are not content with the quality of crisis reporting found on social media, then they should do something about it by implementing the appropriate policies to create the demand for higher quality and more structured reporting. The first emergency telephone service was launched in London some 80 years ago in response to a devastating fire. At the time, the idea of using a phone to report emergencies was controversial. Today, the London Fire Brigade is paving the way forward by introducing Twitter as a reporting channel. This move may seem controversial to some today, but give it a few years and people will look back and ask what took us so long to adopt new social media channels for crisis reporting.

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Data Science for Social Good and Humanitarian Action

My (new) colleagues at the University of Chicago recently launched a new and exciting program called “Data Science for Social Good”. The program, which launches this summer, will bring together dozens top-notch data scientists, computer scientists an social scientists to address major social challenges. Advisors for this initiative include Eric Schmidt (Google), Raed Ghani (Obama Administration) and my very likable colleague Jake Porway (DataKind). Think of “Data Science for Social Good” as a “Code for America” but broader in scope and application. I’m excited to announce that QCRI is looking to collaborate with this important new program given the strong overlap with our Social Innovation Vision, Strategy and Projects.

My team and I at QCRI are hoping to mentor and engage fellows throughout the summer on key humanitarian & development projects we are working on in partnership with the United Nations, Red Cross, World Bank and others. This would provide fellows with the opportunity to engage in  “real world” challenges that directly match their expertise and interests. Second, we (QCRI) are hoping to replicate this type of program in Qatar in January 2014.

Why January? This will give us enough time to design the new program based on the result of this summer’s experiment. More importantly, perhaps, it will be freezing in Chicago ; ) and wonderfully warm in Doha. Plus January is an easier time for many students and professionals to take “time off”. The fellows program will likely be 3 weeks in duration (rather than 3 months) and will focus on applying data science to promote social good projects in the Arab World and beyond. Mentors will include top Data Scientists from QCRI and hopefully the University of Chicago. We hope to create 10 fellowship positions for this Data Science for Social Good program. The call for said applications will go out this summer, so stay tuned for an update.

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How To Disconnect in a Hyper Connected World

Disconnecting in a hyper connected world is already challenging enough if not near impossible. An exploding inbox does not help. No one wants to come back from quality time off only to find an inbox of 5,000+ unread messages. Moreover, the very thought of having thousands of emails piling up is stressful and inevitably results in checking emails. This in turns sucks you right back into work mode, which blows. I have a solution. Read on.

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I will be away from April 28th until May 12th. During this time, I will disconnect and be offline. In order to truly enjoy complete peace of mind during these precious days, I shall set up an automated email reply with a link to this post. So if you’ve found your way here from said reply, here’s the full message and catch: I’m off-grid until May 12th. This means that any emails that come in between April 28th and May 12th will be *automatically deleted*. I do this for peace of mind whilst on vacation, and definitely not because I don’t value what you have to say. So, if your message is important, then please kindly resend it after May 12th. Many thanks for your kind understanding. I promise to return the favor. Lets all help each other find easier ways to disconnect in our hyper connected world.

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Artificial Intelligence for Monitoring Elections (AIME)

Citizen-based, crowdsourced election observation initiatives are on the rise. Leading election monitoring organizations are also looking to leverage citizen-based reporting to complement their own professional election monitoring efforts. Meanwhile, the information revolution continues apace, with the number of new mobile phone subscriptions up by over 1 billion in just the past 36 months alone. The volume of election-related reports generated by “the crowd” is thus expected to grow significantly in the coming years. But international, national and local election monitoring organizations are completely unprepared to deal with the rise of Big (Election) Data.

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The purpose of this collaborative research project, AIME, is to develop a free and open source platform to automatically filter relevant election reports from the crowd. The platform will include pre-defined classifiers (e.g., security incidents,  intimidation, vote-buying, ballot stuffing etc.) for specific countries and will also allow end-users to create their own classifiers on the fly. The project, launched by QCRI and several key partners, will specifically focus on unstructured user-generated content from SMS and Twitter. AIME partners include a major international election monitoring organization and several academic research centers. The AIME platform will use the technology being developed for QCRI’s AIDR project: Artificial Intelligence for Disaster Response.

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Self-Organized Crisis Response to #BostonMarathon Attack

I’m going to keep this blog post technical because the emotions from yesterday’s events are still too difficult to deal with. Within an hour of the bombs going off, I received several emails asking me to comment on the use of social media in Boston and how it differed to the digital humanitarian response efforts I am typically engaged in. So here are just a few notes, nothing too polished, but some initial reactions.

I Stand with Boston

Once again, we saw the outpouring of operational support from the “Crowd” with over two thousand people in the Boston area volunteering to take people in if they needed help, and this within 60 minutes of the attack. This was coordinated via a Google Spreadsheet & Google Form. This is not the first time that these web-based solutions were used for disaster response. For example, Google Spreadsheets was used to coordinate grassroots response efforts during the major Philippine floods in 2012.

We’re not all affected the same way during a crisis and those of us who are less affected almost always look for ways to help. Unlike the era of television broadcasting, the crowd can now become an operational actor in disaster response. To be sure, paid disaster response professionals cannot be everywhere at the same time, but the crowd is always there. This explains I have look called for a “Match.com for disaster response” to match local needs with local resources. So while I received numerous pings on Twitter, Skype and email about launching a crisis map for Boston, I am skeptical that doing so would have added much value.

What was/is needed is real-time filtering of social media content and matching of local needs (information and material needs) with local resources. There are two complementary ways to do this: human computing (e.g., crowdsourcing, microtasking, etc) and machine computing (natural language processing, machine learning, etc), which is why my team and I at QCRI are working on developing these solutions.

Other observations from the response to yesterday’s tragedy:

  • Boston Police made active use of their Twitter account to inform and advise. They also asked other Twitter users to spread their request for everyone to leave the city center area. The police and other emergency services also actively crowdsourced photographs and video footage to begin their criminal investigations. There was such heavy multimedia social media activity in the area that one could no doubt develop a Photosynth rendering of the scene.
  • There were calls for residents to unlock their Wifi networks to enable people in the streets to get access to the Internet. This was especially important after the cellphone network was taken offline for security reasons. To be sure, access to information is equally important as access to water, food, shelter, etc, during a crisis.

I’d welcome any other observation from readers, e.g., similarities and differences between the use of technologies for domestic emergency management versus international humanitarian efforts. I would also be interested to hear thoughts about how the two could be integrated or at the very least learn from each other.

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