My colleague Hemant Purohit at QCRI has been working with us on automatically extracting needs and offers of help posted on Twitter during disasters. When the 2-mile wide, Category 4 Tornado struck Moore, Oklahoma, he immediately began to collect relevant tweets about the Tornado’s impact and applied the algorithms he developed at QCRI to extract needs and offers of help.

As long-time readers of iRevolution will know, this is an approach I’ve been advocating for and blogging about for years, including the auto-matching of needs and offers. These algorithms (classifiers) will also be made available as part of our Artificial Intelligence for Disaster Response (AIDR) platform. In the meantime, we have contacted our colleagues at the American Red Cross’s Digital Operations Center (DigiOps) to offer the results of the processed data, i.e., 1,000+ tweets requesting & offering help. If you are an established organization engaged in relief efforts following the Tornado, please feel free to get in touch with us (patrick@iRevolution.net) so we can make the data available to you. 

As part of QCRI’s Artificial Intelligence for Monitoring Elections (AIME) project, I liaised with Kaggle to work with a top notch Data Scientist to carry out a proof of concept study. As I’ve blogged in the past, crowdsourced election monitoring projects are starting to generate “Big Data” which cannot be managed or analyzed manually in real-time. Using the crowdsourced election reporting data recently collected by Uchaguzi during Kenya’s elections, we therefore set out to assess whether one could use machine learning to automatically tag user-generated reports according to topic, such as election-violence. The purpose of this post is to share the preliminary results from this innovative study, which we believe is the first of it’s kind.

The aim of this initial proof-of-concept study was to create a model to classify short messages (crowdsourced election reports) into several predetermined categories. The classification models were developed by applying a machine learning technique called gradient boosting on word features extracted from the text of the election reports along with their titles. Unigrams, bigrams and the number of words in the text and title were considered in the model development. The tf-idf weighting function was used following internal validation of the model.

The results depicted above confirm that classifiers can be developed to automatically classify short election observation reports crowdsourced from the public. The classification was generated by 10-fold cross validation. Our classifier is able to correctly predict whether a report is related to violence with an accuracy of 91%, for example. We can also accurately predict  89% of reports that relate to “Voter Issues” such as registration issues and reports that indicate positive events, “Fine” (86%).

The plan for this Summer and Fall is to replicate this work for other crowdsourced election datasets from Ghana, Liberia, Nigeria and Uganda. We hope the insights gained from this additional research will reveal which classifiers and/or “super classifiers” are portable across certain countries and election types. Our hypothesis, based on related crisis computing research, is that classifiers for certain types of events will be highly portable. However, we also hypothesize that the application of most classifiers across countries will result in lower accuracy scores. To this end, our Artificial Intelligence for Monitoring Elections platform will allow election monitoring organizations (end users) to create their own classifiers on the fly and thus meet their own information needs.

Big thanks to Nao for his excellent work on this predictive modeling project.

In 2010, Russian volunteers used social media and a live crisis map to crowdsource their own disaster relief efforts as massive forest fires ravaged the country. These efforts were seen by many as both more effective and visible than the government’s response. In 2011, Egyptian volunteers used social media to crowdsource their own humanitarian convoy to provide relief to Libyans affected by the fighting. In 2012, Iranians used social media to crowdsource and coordinate grassroots disaster relief operations following a series of earthquakes in the north of the country. Just weeks earlier, volunteers in Beijing crowd-sourced a crisis map of the massive flooding in the city. That map was immediately available and far more useful than the government’s crisis map. In early 2013, a magnitude 7  earthquake struck Southwest China, killing close to 200 and injuring more than 13,000. The response, which was also crowdsourced by volunteers using social media and mobile phones, actually posed a threat to the Chinese Government.

“Wang Xiaochang sprang into action minutes after a deadly earthquake jolted this lush region of Sichuan Province [...]. Logging on to China’s most popular social media sites, he posted requests for people to join him in aiding the survivors. By that evening, he had fielded 480 calls” (1). While the government had declared the narrow mountain roads to the disaster-affected area blocked to unauthorized rescue vehicles, Wang and hitchhiked his way through with more than a dozen other volunteers. “Their ability to coordinate — and, in some instances, outsmart a government intent on keeping them away — were enhanced by Sina Weibo, the Twitter-like microblog that did not exist in 2008 but now has more than 500 million users” (2). And so, ”While the military cleared roads and repaired electrical lines, the volunteers carried food, water and tents to ruined villages and comforted survivors of the temblor [...]“ (3). Said Wang: “The government is in charge of the big picture stuff, but we’re doing the work they can’t do” (4).

In response to this same earthquake, another volunteer, Li Chengpeng, “turned to his seven million Weibo followers and quickly organized a team of volunteers. They traveled to the disaster zone on motorcycles, by pedicab and on foot so as not to clog roads, soliciting donations via microblog along the way. What he found was a government-directed relief effort sometimes hampered by bureaucracy and geographic isolation. Two days after the quake, Mr. Li’s team delivered 498 tents, 1,250 blankets and 100 tarps — all donated — to Wuxing, where government supplies had yet to arrive. The next day, they hiked to four other villages, handing out water, cooking oil and tents. Although he acknowledges the government’s importance during such disasters, Mr. Li contends that grass-roots activism is just as vital. ‘You can’t ask an NGO to blow up half a mountain to clear roads and you can’t ask an army platoon to ask a middle-aged woman whether she needs sanitary napkins, he wrote in a recent post” (5).

As I’ve blogged in the past (here and here, for example), using social media to crowdsourced grassroots disaster response efforts serves to create social capital and strengthen collective action. This explains why the Chinese government (and others) faced a “groundswell of social activism” that it feared could “turn into government opposition” following the earthquake (6). So the Communist Party tried to turn the disaster into a “rallying cry for political solidarity. ‘The more difficult the circumstance, the more we should unite under the banner of the party,’ the state-run newspaper People’s Daily declared [...], praising the leadership’s response to the earthquake” (7).

This did not quell the rise in online activism, however, which has “forced the government to adapt. Recently, People’s Daily announced that three volunteers had been picked to supervise the Red Cross spending in the earthquake zone and to publish their findings on Weibo. Yet on the ground, the government is hewing to the old playbook. According to local residents, red propaganda banners began appearing on highway overpasses and on town fences even before water and food arrived. ‘Disasters have no heart, but people do,’ some read. Others proclaimed: ‘Learn from the heroes who came here to help the ones struck by disaster’ (8). Meanwhile, the Central Propaganda Department issued a directive to Chinese newspapers and websites “forbidding them to carry negative news, analysis or commentary about the earthquake” (9). Nevertheless, “Analysts say the legions of volunteers and aid workers that descended on Sichuan threatened the government’s carefully constructed narrative about the earthquake. Indeed, some Chinese suspect such fears were at least partly behind official efforts to discourage altruistic citizens from coming to the region” (10).

Aided by social media and mobile phones, grassroots disaster response efforts present a new and more poignant “Dictator’s Dilemma” for repressive regimes. The original Dictator’s Dilemma refers to an authoritarian government’s competing interest in using information communication technology by expanding access to said technology while seeking to control the democratizing influences of this technology. In contrast, the “Dictator’s Disaster Lemma” refers to a repressive regime confronted with effectively networked humanitarian response at the grassroots level, which improves collective action and activism in political contexts as well. But said regime cannot prevent people from helping each other during natural disasters as this could backfire against the regime.

See also:

 •  How Civil Disobedience Improves Crowdsourced Disaster Response [Link]

My colleagues and I at QCRI and the Masdar Institute will be launching Verily in the near future. The project has already received quite a bit of media coverage—particularly after the Boston marathon bombings. So here’s an update. While major errors were made in the crowdsourced response to the bombings, social media can help to find quickly find individuals and resources during a crisis. Moreover, time-critical crowdsourcing can also be used to verify unconfirmed reports circulating on social media.

The errors made following the bombings were the result of two main factors:

(1) the crowd is digitally illiterate
(2) the platforms used were not appropriate for the tasks at hand

The first factor has to do with education. Most of us are still in Kindergarden when it comes to the appropriate use social media. We lack the digital or media literacy required for the responsible use of social media during crises. The good news, however, is that the major backlash from the mistakes made in Boston are already serving as an important lesson to many in the crowd who are very likely to think twice about retweeting certain content or making blind allegations on social media in the future. The second factor has to do with design. Tools like Reddit and 4Chan that are useful for posting photos of cute cats are not always the tools best designed for finding critical information during crises. The crowd is willing to help, this much has been proven. The crowd simply needs better tools to focus and rationalize to goodwill of it’s members.

Verily was inspired from the DARPA Red Balloon Challenge which leveraged social media & social networks to find the location of 10 red weather balloons planted across the continental USA (3 million square miles) in under 9 hours. So Verily uses that same time-critical mobilization approach—negative incentive recursive mechanism—to rapidly collect evidence around a particular claim during a disaster, such as “The bridge in downtown LA has been destroyed by the earthquake”. Users of Verily can share this verification challenge directly from the Verily website (e.g., Share via Twitter, FB, and Email), which posts a link back to the Verily claim page.

This time-critical mobilization & crowdsourcing element is the first main component of Verily. Because disasters are far more geographically bounded than the continental US, we believe that relevant evidence can be crowdsourced in a matter of minutes rather than hours. Indeed, while the degree of separation in the analog world is 6, that number falls closer to 4 on social media, and we believe falls even more in bounded geographical areas like urban centers. This means that the 20+ people living opposite that bridge in LA are only 2 or 3 hops from your social network and could be tapped via Verily to take pictures of the bridge from their window, for example.

The second main component is to crowdsource critical thinking which is key to countering the spread of false rumors during crises. The interface to post evidence on Verily is modeled along the lines of Pinterest, but with each piece of content (text, image, video), users are required to add a sentence or two to explain why they think or know that piece of evidence is authentic or not. Others can comment on said evidence accordingly. This workflow prompts users to think critically rather than blindly share/RT content on Twitter without much thought, context or explanation. Indeed, we hope that with Verily more people will share links back to Verily pages rather than to out of context and unsubstantiated links of images/videos/claims, etc.

In other words, we want to redirect traffic to a repository of information that incentivises critical thinking. This means Verily is also looking to be an educational tool; we’ll have simple mini-guides on information forensics available to users (drawn from the BBC’s UGC, NPR’s Andy Carvin, etc). While we’ll include dig ups/downs on perceived authenticity of evidence posted to Verily, this is not the main focus of Verily. Dig ups/downs are similar to retweets and simply do not capture/explain whether said digger has voted based on her/his expertise or any critical thinking.

If you’re interested in supporting this project and/or sharing feedback, then please feel free to contact me at any time. For more background information on Verily, kindly see this post.

 

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.