Category Archives: Digital Activism

Why Digital Social Capital Matters for Disaster Resilience and Response

Recent empirical studies have clearly demonstrated the importance of offline social capital for disaster resilience and response. I’ve blogged about some of this analysis here and here. Social capital is typically described as those “features of social organizations, such as networks, norms, and trust, that facilitate action and cooperation for mutual benefit.” In other words, social capital increases a group’s capacity for collective action and thus self-organization, which is a key driver of disaster resilience. What if those social organizations were virtual and the networks digital? Would these online communities “generate digital social capital”? And would this digital social capital have any impact on offline social capital, collective action and resilience?

Social Capital

A data-driven study published recently, “Social Capital and Pro-Social Behavior Online and Offline” (PDF), presents some fascinating insights. The study, carried out by Constantin M. Bosancianu, Steve Powell and Esad Bratovi, draws on their survey of 1,912 Internet users in Bosnia & Herzegovina, Croatia and Serbia. The authors specifically consider two types of social capital: bonding social capital and bridging social capital. “

“Bridging social capital is described as inclusive, fostered in networks where membership is not restricted to a particular group defined by strict racial, class, linguistic or ethnic criteria.  Regular interactions inside these networks would gradually build norms of generalized trust and reciprocity at the individual level. These relationships […] are able to offer the individual access to new information but are not very adept in providing emotional support in times of need.”

“Bonding social capital, on the other hand, is exclusive, fostered in tight-knit networks of family members and close friends. Although the degree of information redundancy in these networks is likely high (as most members occupy the same social space), they provide […] the “sociological superglue” which gets members through tough emotional stages in their lives.”

The study’s findings reveal that online and offline social capital were correlated with each other. More specifically, online bridging social capital was closely correlated with offline bridging social capital, while online binding social capital was closely correlated with offline binding social capital. Perhaps of most interest with respect to disaster resilience, the authors discovered that “offline bridging social capital can benefit from online interactions.”


Big Data: Sensing and Shaping Emerging Conflicts

The National Academy of Engineering (NAE) and US Institute of Peace (USIP) co-organized a fascinating workshop on “Sensing & Shaping Emerging Conflicts” in November 2012. I had the pleasure of speaking at this workshop, the objective of which was to “identify major opportunities and impediments to providing better real-time information to actors directly involved in situations that could lead to deadly violence.” We explored “several scenarios of potential violence drawn from recent country cases,” and “considered a set of technologies, applications and strategies that have been particularly useful—or could be, if better adapted for conflict prevention.” 


The workshop report was finally published this week. If you don’t have time to leaf through the 40+page study, then the following highlights may be of interest. One of the main themes to emerge was the promise of machine learning (ML), a branch of Artificial Intelligence (AI). These approaches “continue to develop and be applied in un-anticipated ways, […] the pressure from the peacebuilding community directed at technology developers to apply these new technologies to the cause of peace could have tremendous benefits.” On a personal note, this is one of the main reasons I joined the Qatar Computing Research Institute (QCRI); namely to apply the Institute’s expertise in ML and AI to the cause of peace, development and disaster relief.

“As an example of the capabilities of new technologies, Rafal Rohozinski, principal with the SecDev Group, described a sensing exercise focused on Syria. Using social media analytics, his group has been able to identify the locations of ceasefire violations or regime deployments within 5 to 15 minutes of their occurrence. This information could then be passed to UN monitors and enable their swift response. In this way, rapid deductive cycles made possible through technology can contribute to rapid inductive cycles in which short-term predictions have meaningful results for actors on the ground. Further analyses of these events and other data also made it possible to capture patterns not seen through social media analytics. For example, any time regime forces moved to a particular area, infrastructure such as communications, electricity, or water would degrade, partly because the forces turned off utilities, a normal practice, and partly because the movement of heavy equipment through urban areas caused electricity systems go down. The electrical grid is connected to the Internet, so monitoring of Internet connections provided immediate warnings of force movements.”

This kind of analysis may not be possible in many other contexts. To be sure, the challenge of the “Digital Divide” is particularly pronounced vis-a-vis the potential use of Big Data for sensing and shaping emerging conflicts. That said, my colleague Duncan Watts “clarified that inequality in communications technology is substantially smaller than other forms of inequality, such as access to health care, clean water, transportation, or education, and may even help reduce some of these other forms of inequality. Innovation will almost always accrue first to the wealthier parts of the world, he said, but inequality is less striking in communications than in other areas.” By 2015, for example, Sub-Saharan Africa will have more people with mobile network access than with electricity at home.

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My colleague Chris Spence from NDI also presented at the workshop. He noted the importance of sensing the positive and not just the negative during an election. “In elections you want to focus as much on the positive as you do on the negative and tell a story that really does convey to the public what’s actually going on and not just a … biased sample of negative reports.” Chris also highlighted that “one problem with election monitoring is that analysts still typically work with the software tools they used in the days of manual reporting rather than the Web-based tools now available. There’s an opportunity that we’ve been trying to solve, and we welcome help.” Building on our expertise in Machine Learning and Artificial Intelligence, my QCRI colleagues and I want to develop classifiers that automatically categorize large volumes of crowdsourced election reports. So I’m exploring this further with Chris & NDI. Check out the Artificial Intelligence for Monitoring Elections (AIME) project for more information.

One of the most refreshing aspects of the day-long workshop was the very clear distinction made between warning and response. As colleague Sanjana Hattotuwa cautioned: “It’s an open question whether some things are better left unsaid and buried literally and metaphorically.”  Duncan added that, “The most important question is what to do with information once it has been gathered.” Indeed, “Simply giving people more information doesn’t necessarily lead to a better outcome, although some-times it does.” My colleague Dennis King summed it up very nicely, “Political will is not an icon on your computer screen… Generating political will is the missing factor in peacebuilding and conflict resolution.”

In other words, “the peacebuilding community often lacks actionable strategies to convert sensing into shaping,” as colleague Fred Tipson rightly noted. Libbie Prescott, who served as strategic advisor to the US Secretary of State and participated in the workshop, added: “Policymakers have preexisting agendas, and just presenting them with data does not guarantee a response.” As my colleague Peter Walker wrote in a book chapter published way back in 1992, “There is little point in investing in warning systems if one then ignores the warnings!” To be clear, “early warning should not be an end in itself; it is only a tool for preparedness, prevention and mitigation with regard to disasters, emergencies and conflict situations, whether short or long term ones. […] The real issue is not detecting the developing situation, but reacting to it.”

Now Fast froward to 2013: OCHA just published this groundbreaking report confirming that “early warning signals for the Horn of Africa famine in 2011 did not produce sufficient action in time, leading to thousands of avoidable deaths. Similarly, related research has shown that the 2010 Pakistan floods were predictable.” As DfID notes in this 2012 strategy document, “Even when good data is available, it is not always used to inform decisions. There are a number of reasons for this, including data not being available in the right format, not widely dispersed, not easily accessible by users, not being transmitted through training and poor information management. Also, data may arrive too late to be able to influence decision-making in real time operations or may not be valued by actors who are more focused on immediate action” (DfID)So how do we reconcile all this with Fred’s critical point: “The focus needs to be on how to assist the people involved to avoid the worst consequences of potential deadly violence.”


The fact of the matter is that this warning-response gap in the field of conflict prevention is over 20 years old. I have written extensively about the warning-response problem here (PDF) and here (PDF), for example. So this challenge is hardly a new one, which explains why a number of innovative and promising solutions have been put forward of the years, e..g, the decentralization of conflict early warning and response. As my colleague David Nyheim wrote five years ago:

A state-centric focus in conflict management does not reflect an understanding of the role played by civil society organisations in situations where the state has failed. An external, interventionist, and state-centric approach in early warning fuels disjointed and top down responses in situations that require integrated and multilevel action.” He added: “Micro-level responses to violent conflict by ‘third generation early warning systems’ are an exciting development in the field that should be encouraged further. These kinds of responses save lives.”

This explains why Sanjana is right when he emphasizes that “Technology needs to be democratized […], made available at the lowest possible grassroots level and not used just by elites. Both sensing and shaping need to include all people, not just those who are inherently in a position to use technology.” Furthermore, Fred is spot on when he says that “Technology can serve civil disobedience and civil mobilization […] as a component of broader strategies for political change. It can help people organize and mobilize around particular goals. It can spread a vision of society that contests the visions of authoritarian.”

In sum, As Barnett Rubin wrote in his excellent book (2002) Blood on the Doorstep: The Politics of Preventive Action, “prevent[ing] violent conflict requires not merely identifying causes and testing policy instruments but building a political movement.” Hence this 2008 paper (PDF) in which I explain in detail how to promote and facilitate technology-enabled civil resistance as a form of conflict early response and violence prevention.


See Also:

  • Big Data for Conflict Prevention [Link]

Using Crowdring for Disaster Response?

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:

  1. Do you have enough drinking water? 
  2. Are humanitarian organizations doing a good job?
  3. 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.


How Crowdsourced Disaster Response in China Threatens the Government

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]

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.


Jointly_App-02 Jointly_App-03


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: should you be interested in learning more or collaborating with her.


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 “ 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.


Humanitarianism in the Network Age: Groundbreaking Study

My colleagues at the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) have just published a groundbreaking must-read study on Humanitarianism in the Network Age; an important and forward-thinking policy document on humanitarian technology and innovation. The report “imagines how a world of increasingly informed, connected and self-reliant communities will affect the delivery of humanitarian aid. Its conclusions suggest a fundamental shift in power from capital and headquarters to the people [that] aid agencies aim to assist.” The latter is an unsettling prospect for many. To be sure, Humanitarianism in the Network Age calls for “more diverse and bottom-up forms of decision-making—something that most Governments and humanitarian organizations were not designed for. Systems constructed to move information up and down hierarchies are facing a new reality where information can be generated by any-one, shared with anyone and acted by anyone.”

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The purpose of this blog post (available as a PDF) is to summarize the 120-page OCHA study. In this summary, I specifically highlight the most important insights and profound implications. I also fill what I believe are some of the report’s most important gaps. I strongly recommend reading the OCHA publication in full, but if you don’t have time to leaf through the study, reading this summary will ensure that you don’t miss a beat. Unless otherwise stated, all quotes and figures below are taken directly from the OCHA report.

All in all, this is an outstanding, accurate, radical and impressively cross-disciplinary study. In fact, what strikes me most about this report is how far we’ve come since the devastating Haiti Earthquake of 2010. Just three short years ago, speaking the word “crowdsourcing” was blasphemous, like “Voldermort” (for all you Harry Potter fans). This explains why some humanitarians called me the CrowdSorcerer at the time (thinking it was a derogatory term). CrisisMappers was only launched three months before Haiti. The Standby Volunteer Task Force (SBTF) didn’t even exist at the time and the Digital Humanitarian Network (DHN) was to be launched 2 years hence. And here we are, just three short years later, with this official, high-profile humanitarian policy document that promotes crowdsourcing, digital humanitarian response and next generation humanitarian technology. Exciting times. While great challenges remain, I dare say we’re trying our darned best to find some solutions, and this time through collaboration, CrowdSorcerers and all. The OCHA report is a testament to this collaboration.

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the Rise of big (crisis) data

Over 100 countries have more mobile phone subscriptions than they have people. One in four individuals in developing countries use the Internet. This figure will double within 20 months. About 70% of Africa’s total population are mobile subscribers. In short, “The planet has gone online, producing and sharing vast quantities of information.” Meanwhile, however, hundreds of millions of people are affected by disasters every year—more than 250 million in 2010 alone. There have been over 1 billion new mobile phone subscriptions since 2010. In other words, disaster affected communities are becoming increasingly “digital” as a result of the information revolution. These new digital technologies continue are evolving new nervous system for our planet, taking the pulse of our social, economic and political networks in real-time.

“Filipinos sent an average of 2 billion SMS messages every day in early 2012,” for example. When disaster strikes, many of these messages are likely to relay crisis information. In Japan, over half-a-million new users joined Twitter the day after the 2011 Earthquake. More than 177 million tweets about the disaster were posted that same day—that is, 2,000 tweets per second on average. Welcome to “The Rise of Big (Crisis) Data.” Meanwhile, back in the US, 80% of the American public expects emergency responders to monitor social media; and almost as many expect them to respond within three hours of posting a request on social media (1). These expectations have been shown to increase year-on year. “At the same time,” however, the OCHA report notes that “there are greater numbers of people […] who are willing and able to respond to needs.”

communities first

A few brave humanitarian organizations are embracing these changes and new realities, “reorienting their approaches around the essential objectives of helping people to help themselves.” That said, “the frontline of humanitarian action has always consisted of communities helping themselves before outside aid arrives.” What is new, however, is “affected people using technology to communicate, interact with and mobilize their social networks quicker than ever before […].” To this end, “by rethinking how aid agencies work and communicate with people in crisis, there is a chance that many more lives can be saved.” In sum, “the increased reach of communications networks and the growing network of people willing and able to help, are defining a new age—a network age—for humanitarian assistance.”

This stands in stark contrast to traditional notions of humanitarian assistance, which refer to “a small group of established international organizations, often based in and funded by high-income countries, providing help to people in a major crisis. This view is now out of date.” As my colleague Tim McNamara noted on the CrisisMappers list-serve, (cited in the OCHA report), this is “…not simply a technological shift [but] also a process of rapid decentralization of power. With extremely low barriers to entry, many new entrants are appearing in the fields of emergency and disaster response. They are ignoring the traditional hierarchies, because the new entrants perceive that there is something they can do which benefits others.” In other words, the humanitarian “world order” is shifting towards a more multipolar system. And so, while Tim was “referring to the specific case of volunteer crisis mappers […], the point holds true across all types of humanitarian work.”

Take the case of Somalia Speaks, for example. A journalist recently asked me to list the projects I am most proud of in this field. Somalia Speaks ranks very high. I originally pitched the idea to my Al-Jazeera colleagues back in September 2011; the project was launched three months later. Together with my colleagues at Souktelwe texted 5,000 Somalis across the country to ask how were personally affected by the crisis.


As the OCHA study notes, we received over 3,000 responses, which were translated into English and geotagged by the Diaspora and subsequently added to a crisis map hosted on the Al-Jazeera website. From the OCHA report: “effective communication can also be seen as an end itself in promoting human dignity. More than 3,000 Somalis responded to the Somalia Speaks project, and they seemed to feel that speaking out was a worthwhile activity.” In sum, “The Somalia Speaks project enabled the voices of people from one of the world’s most inaccessible, conflict-ridden areas, in a language known to few outside their community, to be heard by decision makers from across the planet.” The project has since been replicated several times; see Uganda Speaks for example. The OCHA study refers to Somalia Speaks at least four times, highlighting the project as an example of networked humanitarianism.


The report also emphasizes the critical importance of data security, privacy and protection in the network age. OCHA’s honest and balanced approach to the topic is another reason why this report is so radical and forward thinking. “Concern over the protection of information and data is not a sufficient reason to avoid using new communications technologies in emergencies, but it must be taken into account. To adapt to increased ethical risks, humanitarian responders and partners need explicit guidelines and codes of conduct for managing new data sources.” This is precisely why I worked with GSMA’s Disaster Response Program to draft and publish the first ever Code of Conduct for the Use of SMS in Disaster Response. I have also provided extensive feedback to the International Committee of the Red Cross’s (ICRC) latest edition of the “Professional Standards for Protection Work,” which was just launched in Geneva this month. My colleagues Emmanuel Letouzé and Patrick Vinck also included a section on data security and ethics in our recent publication on the use of Big Data for Conflict Prevention. In addition, I have blogged about this topic quite a bit: herehere and here, for example.

crisis in decision making

“As the 2010 Haiti crisis revealed, the usefulness of new forms of information gathering is limited by the awareness of responders that new data sources exist, and their applicability to existing systems of humanitarian decision-making.” The fact of the matter is that humanitarian decision-making structures are simply not geared towards using Big Crisis Data let alone new data sources. More pointedly, however, humanitarian decision-making processes are often not based on empirical data in the first place, even when the data originate from traditional sources. As DfID notes in this 2012 strategy document, “Even when good data is available, it is not always used to inform decisions. There are a number of reasons for this, including data not being available in the right format, not widely dispersed, not easily accessible by users, not being transmitted through training and poor information management. Also, data may arrive too late to be able to influence decision-making in real time operations or may not be valued by actors who are more focused on immediate action.”

This is the classic warning-response gap, which has been discussed ad nauseum for decades in the field of famine early warning systems and conflict early warning systems. More data in no way implies action. Take the 2011 Somalia Famine, which was one of the best documented crises yet. So the famine didn’t occur because data was lacking. “Would more data have driven a better decision making process that could have averted disaster? Unfortunately, this does not appear to be the case. There had, in fact, been eleven months of escalating warnings emanating from the famine early warning systems that monitor Somalia. Somalia was, at the time, one of the most frequently surveyed countries in the world, with detailed data available on malnutrition prevalence, mortality rates, and many other indicators. The evolution of the famine was reported in almost real time, yet there was no adequate scaling up of humanitarian intervention until too late” (2).

At other times, “Information is sporadic,” which is why OCHA notes that “decisions can be made on the basis of anecdote rather than fact.” Indeed, “Media reports can significantly influence allocations, often more than directly transmitted community statements of need, because they are more widely read or better trusted.” (It is worth keeping in mind that the media makes mistakes; the New York Times alone makes over 7,000 errors every year). Furthermore, as acknowledged, by OCHA, “The evidence suggests that new information sources are no less representative or reliable than more traditional sources, which are also imperfect in crisis settings.” This is one of the most radical statements in the entire report. OCHA should be applauded for their remarkable fortitude in plunging into this rapidly shifting information landscape. Indeed, they go on to state that, “Crowdsourcing has been used to validate information, map events, translate text and integrate data useful to humanitarian decision makers.”

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The vast major of disaster datasets are not perfect, regardless of whether they are drawn from traditional or non-traditional sources. “So instead of criticizing the lack of 100% data accuracy, we need to use it as a base and ensure our Monitoring and Evaluation (M&E) and community engagement pieces are strong enough to keep our programming relevant” (Bartosiak 2013). And so, perhaps the biggest impact of new technologies and recent disasters on the humanitarian sector is the self disrobing of the Emperor’s Clothes (or Data). “Analyses of emergency response during the past five years reveal that poor information management has severely hampered effective action, costing many lives.” Disasters increasingly serve as brutal audits of traditional humanitarian organizations; and the cracks are increasingly difficult to hide in an always-on social media world. The OCHA study makes clear that  decision-makers need to figure out “how to incorporate these sources into decisions.”

Fact is, “To exploit the opportunity of the network age, humanitarians must understand how to use the new range of available data sources and have the capacity to transform this data into useful information.” Furthermore, it is imperative “to ensure new partners have a better understanding of how [these] decisions are made and what information is useful to improve humanitarian action.” These new partners include the members of the Digital Humanitarian Network (DHN), for example. Finally, decision-makers also need to “invest in building analytic capacity across the entire humanitarian network.” This analytic capacity can no longer rest on manual solutions alone. The private sector already makes use of advanced computing platforms for decision-making purposes. The humanitarian industry would be well served to recognize that their problems are hardly unique. Of course, investing in greater analytic capacity is an obvious solution but many organizations are already dealing with limited budgets and facing serious capacity constraints. I provide some creative solutions to this challenge below, which I refer to as “Data Science Philanthropy“.


Near Perfection

OCHA’s report is brilliant, honest and forward thinking. This is by far the most important official policy document yet on humanitarian technology and digital humanitarian response—and thus on the very future of humanitarian action. The study should be required reading for everyone in the humanitarian and technology communities, which is why I plan to organize a panel on the report at CrisisMappers 2013 and will refer to the strategy document in all of my forthcoming talks and many a future blog post. In the meantime, I would like to highlight and address a some of the issues that I feel need to be discussed to take this discussion further.

Ironically, some of these gaps appear to reflect a rather limited understanding of advanced computing & next generation humanitarian technology. The following topics, for example, are missing from the OCHA report: Microtasking, Sentiment Analysis and Information Forensics. In addition, the report does not relate OCHA’s important work to disaster resilience and people-centered early warning. So I’m planning to expand on the OCHA report in the technology chapter for this year’s World Disaster Report (WDR 2013). This high-profile policy document is an ideal opportunity to amplify OCHA’s radical insights and to take these to their natural and logical conclusions vis-à-vis Big (Crisis) Data. To be clear, and I must repeat this, the OCHA report is the most important forward thinking policy document yet on the future of humanitarian response. The gaps I seek to fill in no way make the previous statement any less valid. The team at OCHA should be applauded, recognized and thanked for their tremendous work on this report. So despite some of the key shortcomings described below, this policy document is by far the most honest, enlightened and refreshing look at the state of the humanitarian response today; a grounded and well-researched study that provides hope, leadership and a clear vision for the future of humanitarianism in the network age.


OCHA recognizes that “there is a significant opportunity to use big data to save lives,” and they also get that, “finding ways to make big data useful to humanitarian decision makers is one of the great challenges, and opportunities, of the network age.” Moreover, they realize that “While valuable information can be generated anywhere, detecting the value of a given piece of data requires analysis and understanding.” So they warn, quite rightly, that “the search for more data can obscure the need for more analysis.” To this end, they correctly conclude that “identifying the best uses of crowdsourcing and how to blend automated and crowdsourced approaches is a critical area for study.” But the report does not take these insights to their natural and logical conclusions. Nor does the report explore how to tap these new data sources let alone analyze them in real time.

Yet these Big Data challenges are hardly unique. Our problems in the humanitarian space are not that “special” or  different. OCHA rightly notes that “Understanding which bits of information are valuable to saving lives is a challenge when faced with this ocean of data.” Yes. But such challenges have been around for over a decade in other disciplines. The field of digital disease detection, for example, is years ahead when it comes to real-time analysis of crowdsourced big data, not to mention private sector companies, research institutes and even new startups whose expertise is Big Data Analytics. I can also speak to this from my own professsional experience. About a decade ago, I worked with a company specializing in conflict forecasting and early using Reuters news data (Big Data).

In sum, the OCHA report should have highlighted the fact that solutions to many of these Big Data challenges already exist, which is precisely why I joined the Qatar Computing Research Institute (QCRI). What’s more, a number of humanitarian technology projects at QCRI are already developing prototypes based on these solutions; and OCHA is actually the main partner in one such project, so it is a shame they did not get credit for this in their own report.

sentiment analysis

While I introduced the use of sentiment analysis during the Haiti Earthquake, this has yet to be replicated in other humanitarian settings. Why is sentiment analysis key to humanitarianism in the network age? The answer is simple: “Communities know best what works for them; external actors need to listen and model their response accordingly.” Indeed, “Affected people’s needs must be the starting point.” Actively listening to millions of voices is a Big Data challenge that has already been solved by the private sector. One such solution is real-time sentiment analysis to capture brand perception. This is a rapidly growing multimillion dollar market, which is why many companies like Crimson Hexagon exist. Numerous Top 500 Fortune companies have been actively using automated sentiment analysis for years now. Why? Because these advanced listening solutions enable them to better understand customer perceptions.

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In Haiti, I applied this approach to tens of thousands of text messages sent by the disaster-affected population. It allowed us to track the general mood of this population on a daily basis. This is important because sentiment analysis as a feedback loop works particularly well with Big Data, which explains why the private sector is all over it. If just one or two individuals in a community are displeased with service delivery during a disaster, they may simply be “an outlier”  or perhaps exaggerating. But if the sentiment analysis at the community level suddenly starts to dip, then this means hundreds, perhaps thousands of affected individuals are now all feeling the same way about a situation. In other words, sentiment analysis serves as a triangulating mechanism. The fact that the OCHA report makes no mention of this existing solution is unfortunate since sentiment feedback loops enable organizations to assess the impact of their interventions by capturing their clients’ perceptions.

Information forensics

“When dealing with the vast volume and complexity of information available in the network age, understanding how to assess the accuracy and utility of any data source becomes critical.” Indeed, and the BBC’s User-Generated Content (UGC) Hub has been doing just this since 2005—when Twitter didn’t even exist. The field of digital information forensics may be new to the humanitarian sector, but that doesn’t mean it is new to every other sector on the planet. Furthermore, recent research on crisis computing has revealed that the credibility of social media reporting can be modeled and even predicted. Twitter has even been called a “Truth Machine” because of the self-correcting dynamic that has been empirically observed. Finally, one of QCRI’s humanitarian technology projects, Verily, focuses precisely on the issue of verifying crowdsourced social media information from social media. And the first organization I reached out to for feedback on this project was OCHA.


The OCHA report overlooks microtasking as well. Yes, the study does address and promote the use of crowdsourcing repeatedly, but again, this  tends to focus on the collection of information rather than the processing of said information. Microtasking applications in the humanitarian space are not totally unheard of, however. Microtasking was used to translate and geolocate tens of thousands of text messages following the Haiti Earthquake. (As the OCHA study notes, “some experts estimated that 90 per cent [of the SMS’s] were ‘repetition’, or ‘white noise’, meaning useless chatter”). There have been several other high profile uses of microtasking for humanitarian operations such as this one thanks to OCHA’s leadership in response to Typhoon Pablo. In sum, microtasking has been used extensively in other sectors to manage the big data and quality control challenge for many years now. So this important human computing solution really ought to have appeared in the OCHA report along with the immense potential of microtasking humanitarian information using massive online multiplayer games (more here).

Open Data is Open Power

OCHA argues that “while information can be used by anyone, power remains concentrated in the hands of a limited number of decision makers.” So if the latter “do not use this information to make decisions in the interests of the people they serve, its value is lost.” I don’t agree that the value is lost. One of the reports’ main themes is the high-impact agency and ingenuity of disaster-affected communities. As OCHA rightly points out, “The terrain is continually shifting, and people are finding new and brilliant ways to cope with crises every day.” Openly accessible crisis information posted on social media has already been used by affected populations for almost a decade now. In other words, communities affected by crises are (quite rightly) taking matters into their own hands in today’s networked world—just like they did in the analog era of yesteryear. As noted earlier, “affected people [are] using technology to communicate, interact with and mobilize their social networks quicker than ever before […].” This explains why “the failure to share [information] is no longer a matter of institutional recalcitrance: it can cost lives.”

creative partnerships

The OCHA study emphasizes that “Humanitarian agencies can learn from other agencies, such as fire departments or militaries, on how to effectively respond to large amounts of often confusing information during a fast-moving crisis.” This is spot on. Situational awareness is first and foremost a military term. The latest Revolution in Military Affairs (RMA) provides important insights into the future of humanitarian technology—see these recent developments, for example. Mean-while, the London Fire Brigade has announced plans to add Twitter as a communication channel, which means city residents will have the option of reporting a fire alert via Twitter. Moreover, the 911 service in the US (999 in the UK) is quite possibly the oldest and longest running crowdsourced emergency service in the world. So there much that humanitarian can learn from 911. But the fact of the matter is that most domestic emergency response agencies are completely unprepared to deal with the tidal wave of Big (Crisis) Data, which is precisely why the Fire Department of New York City (FDNY) and San Francisco City’s Emergency Response Team have recently reached out to me.

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But some fields are way ahead of the curve. The OCHA report should thus have pointed to crime mapping and digital disease detection since these fields have more effectively navigated the big data challenge. As for the American Red Cross’s Digital Operations Center, the main technology they are using, Radian6, has been used by private sector clients for years now. And while the latter can afford the very expensive licensing fees, it is unlikely that cash-strapped domestic emergency response officers and international humanitarian organizations will ever be able to afford these advanced solutions. This is why we need more than just “Data Philanthropy“.

We also need “Data Science Philanthropy“. As the OCHA report states, decision-makers need to “invest in building analytic capacity across the entire humanitarian network.” This is an obvious recommendation, but perhaps not particularly realistic given the limited budgets and capacity constraints in the humanitarian space. This means we need to create more partnerships with Data Science groups like DataKind, Kaggle and the University of Chicago’s Data Science for Social Good program. I’m in touch with these groups and others for this reason. I’ve also been (quietly) building a global academic network called “Data Science for Humanitarian Action” which will launch very soon. Open Source solutions are also imperative for building analytic capacity, which is why the humanitarian technology platforms being developed by QCRI will all be Open Source and freely available.


This points to the following gap in the OCHA report: there is no reference whatsoever to resilience. While the study does recognize that collective self-help behavior is typical in disaster response and should be amplified, the report does not make the connection that this age-old mutual-aid dynamic is the humanitarian sector’s own lifeline during a major disaster. Resilience has to do with a community’s capacity for self-organization. Communication technologies increasingly play a pivotal role in self-organization. This explains why disaster preparedness and disaster risk reduction programs ought to place greater emphasis on building the capacity of at-risk communities to self-organize and mitigate the impact of disasters on their livelihoods. More about this here. Creating resilience through big data is also more academic curiosity, as explained here.


As more and more disaster-affected communities turn to social media in time of need, “Governments and responders will soon need answers to the questions: ‘Where were you? We Facebooked/tweeted/texted for help, why didn’t someone come?'” Again, customer support challenges are hardly unique to the humanitarian sector. Private sector companies have had to manage parallel problems by developing more advanced customer service platforms. Some have even turned to crowdsourcing to manage customer support. I blogged about this here to drive the point home that solutions to these humanitarian challenges already exist in other sectors.

Yes, that’s right, I am promoting the idea of crowdsourcing crisis response. Fact is, disaster response has always been crowdsourced. The real first responders are the disaster affected communities themselves. Thanks to new technologies, this crowdsourced response can be accelerated and made more efficient. And yes, there’s an app (in the making) for that: MatchApp. This too is a QCRI humanitarian technology project (in partnership with MIT’s Computer Science and Artificial Intelligence Lab). The purpose of MatchApp is to decentralize disaster response. After all, the many small needs that arise following a disaster rarely require the attention of paid and experienced emergency responders. Furthermore, as a colleague of mine at NYU shared based on her disaster efforts following Hurricane Sandy, “Solving little challenges can make the biggest differences” for disaster-affected communities.

As noted above, more and more individuals believe that emergency responders should monitor social media during disasters and respond accordingly. This is “likely to increase the pressure on humanitarian responders to define what they can and cannot provide. The extent of communities’ desires may exceed their immediate life-saving needs, raising expectations beyond those that humanitarian responders can meet. This can have dangerous consequences. Expectation management has always been important; it will become more so in the network age.”

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“Community early warning systems (CEWS) can buy time for people to implement plans and reach safety during a crisis. The best CEWS link to external sources of assistance and include the pre-positioning of essential supplies.” At the same time, “communities do not need to wait for information to come from outside sources, […] they can monitor local hazards and vulnerabilities themselves and then shape the response.” This sense and shaping capacity builds resilience, which explains why “international humanitarian organizations must embrace the shift of warning systems to the community level, and help Governments and communities to prepare for, react and respond to emergencies using their own resources and networks.”

This is absolutely spot on and at least 7 years old as far as  UN policy goes. In 2006, the UN’s International Strategy for Disaster Risk Reduction (UNISDR) published this policy document advocating for a people-centered approach to early warning and response systems. They defined the purpose of such as systems as follows:

“… to empower individuals and communities threatened by hazards to act in sufficient time and in an appropriate manner so as to reduce the possibility of personal injury, loss of life, damage to property and the environment, and loss of livelihoods.”

Unfortunately, the OCHA report does not drive these insights to their logical conclusion. Disaster-affected communities are even more ill-equipped to manage the rise of Big (Crisis) Data. Storing, let alone analyzing Big Data Analytics in real-time, is a major technical challenge. As noted here vis-à-vis Big Data Analytics on Twitter, “only corporate actors and regulators—who possess both the intellectual and financial resources to succeed in this race—can afford to participate […].” Indeed, only a handful of research institutes have the technical ability and large funding base carry out the real-time analysis of Big (Crisis) Data. My team and I at QCRI, along with colleagues at UN Global Pulse and GSMA are trying to change this. In the meantime, however, the “Big Data Divide” is already here and very real.

information > Food

“Information is not water, food or shelter; on its own, it will not save lives. But in the list of priorities, it must come shortly after these.” While I understand the logic behind this assertion, I consider it a step back, not forward from the 2005 World Disaster Report (WDR 2005), which states that “People need information as much as water, food, medicine or shelter. Information can save lives, livelihoods and resources.” In fact, OCHA’s assertion contradicts an earlier statement in the report; namely that “information in itself is a life-saving need for people in crisis. It is as important as water, food and shelter.” Fact is: without information, how does one know where/when and from whom clean water and food might be available? How does one know which shelters are open, whether they can accommodate your family and whether the road to the shelter is safe to drive on?

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OCHA writes that, “Easy access to data and analysis, through technology, can help people make better life-saving decisions for themselves and mobilize the right types of external support. This can be as simple as ensuring that people know where to go and how to get help. But to do so effectively requires a clear understanding of how information flows locally and how people make decisions.” In sum, access to information is paramount, which means that local communities should have easy access to next generation humanitarian technologies that can manage and analyze Big Crisis Data. As a seasoned humanitarian colleague recently told me, “humanitarians sometimes have a misconception that all aid and relief comes through agencies.  In fact, (especially with things such a shelter) people start to recover on their own or within their communities. Thus, information is vital in assuring that they do this safely and properly.  Think of the Haiti, build-back-better campaign and the issues with cholera outbreaks.”

Them not us

The technologies of the network age should not be restricted to empowering second- and third-level responders. Unfortunately, as OCHA rightly observes, “there is still a tendency for people removed from a crisis to decide what is best for the people living through that crisis.” Moreover, these paid responders cannot be everywhere at the same time. But the crowd is always there. And as OCHA points out, there are “growing groups of people willing able to help those in need;” groups that unlike their analog counterparts of yesteryear now operate in the “network age with its increased reach of communications networks.” So information is not simply or “primarily a tool for agencies to decide how to help people, it must be understood as a product, or service, to help affected communities determine their own priorities.” Recall the above definition of people-centered early warning. This definition does not all of a sudden become obsolete in the network age. The purpose of next generation technologies is to “empower individuals and communities threatened by hazards to act in sufficient time and in an appropriate manner so as to reduce the possibility of personal injury, loss of life, damage to property and the environment, and loss of livelihoods.”

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Digital humanitarian volunteers are also highly unprepared to deal with the rise of Big Crisis Data, even though they are at the frontlines and indeed the pioneers of digital response. This explains why the Standby Volunteer Task Force (SBTF), a network of digital volunteers that OCHA refers to half-a-dozen times throughout the report, are actively looking to becoming early adopters of next generation humanitarian technologies. Burn out is a serious issue with digital volunteers. They too require access to these next generation technologies, which is precisely why the American Red Cross equips their digital volunteers with advanced computing platforms as part of their Digital Operations Center. Unfortunately, some humanitarians still think that they can just as easily throw more (virtual) volunteers at the Big Crisis Data challenge. Not only are they terribly misguided but also insensitive, which is why, As OCHA notes, “Using new forms of data may also require empowering technical experts to overrule the decisions of their less informed superiors.” As the OCHA study concludes, “Crowdsourcing is a powerful tool, but ensuring that scarce volunteer and technical resources are properly deployed will take further research and the expansion of collaborative models, such as SBTF.”


So will next generation humanitarian technology solve everything? Of course not, I don’t know anyone naïve enough to make this kind of claim. (But it is a common tactic used by the ignorant to attack humanitarian innovation). I have already warned about techno-centric tendencies in the past, such as here and here (see epilogue). Furthermore, one of the principal findings from this OECD report published in 2008 is that “An external, interventionist, and state-centric approach in early warning fuels disjointed and top down responses in situations that require integrated and multilevel action.” You can throw all the advanced computing technology you want at this dysfunctional structural problem but it won’t solve a thing. The OECD thus advocates for “micro-level” responses to crises because “these kinds of responses save lives.” Preparedness is obviously central to these micro-level responses and self-organization strategies. Shockingly, however, the OCHA study reveals that, “only 3% of humanitarian aid goes to disaster prevention and preparedness,” while barely “1% of all other development assistance goes towards disaster risk reduction.” This is no way to build disaster resilience. I doubt these figures will increase substantially in the near future.

This reality makes it even more pressing to ensure that “responders listen to affected people and find ways to respond to their priorities will require a mindset change.” To be sure, “If aid organizations are willing to listen, learn and encourage innovation on the front lines, they can play a critical role in building a more inclusive and more effective humanitarian system.” This need to listen and learn is why next generation humanitarian technologies are not optional. Ensuring that first, second and third-level responders have access to next generation humanitarian technologies is critical for the purposes of self-help, mutual aid and external response.