Monthly Archives: July 2012

PeopleBrowsr: Next-Generation Social Media Analysis for Humanitarian Response?

As noted in this blog post on “Data Philanthropy for Humanitarian Response,” members of the Digital Humanitarian Network (DHNetwork) are still using manual methods for media monitoring. When the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) activated the Standby Volunteer Task Force (SBTF) to crisis map Libya last year, for example, SBTF volunteers manually monitored hundreds of Twitter handles, news sites for several weeks.

SBTF volunteers (Mapsters) do not have access to a smart microtasking platform that could have distributed the task in more efficient ways. Nor do they have access to even semi-automated tools for content monitoring and information retrieval. Instead, they used a Google Spreadsheet to list the sources they were manually monitoring and turned this spreadsheet into a sign-up sheet where each Mapster could sign on for 3-hour shifts every day. The SBTF is basically doing “crowd computing” using the equivalent of a typewriter.

Meanwhile, companies like Crimson Hexagon, NetBase, RecordedFuture and several others have each developed sophisticated ways to monitor social and/or mainstream media for various private sector applications such as monitoring brand perception. So my colleague Nazila kindly introduced me to her colleagues at PeopleBrowsr after reading my post on Data Philanthropy. Last week, Marc from PeopleBrowsr gave me a thorough tour of the platform. I was definitely impressed and am excited that Marc wants us to pilot the platform in support of the Digital Humanitarian Network. So what’s the big deal about PeopleBrowsr? To begin with, the platform has access to 1,000 days of social media data and over 3 terabytes of social data per month.

To put this in terms of information velocity, PeopleBrowsr receives 10,000 social media posts per second from a variety of sources including Twitter, Facebook, fora and blogs. On the latter, they monitor posts from over 40 million blogs including all of Tumblr, Posterious, Blogspot and every WordPress-hosted site. They also pull in content from YouTube and Flickr. (Click on the screenshots below to magnify them).

Lets search for the term “tsunami” on Twitter. (One could enter a complex query, e.g., and/or, not, etc., and also search using twitter handles, word or hashtag clouds, top URLs, videos, pictures, etc). PeopleBrowsr summarizes the result by Location and Community. Location simply refers to where those generating content referring to a tsunami are located. Of course, many Twitter users may tweet about an event without actually being eye-witness accounts (think of Diaspora groups, for example). While PeopleBrowsr doesn’t geo-tag the location of reports events, you can very easily and quickly identify which twitter users are tweeting the most about a given event and where they are located.

As for Community, PeopleBrowsr has  indexed millions of social media users and clustered them into different communities based on their profile/bio information. Given our interest in humanitarian response, we could create our own community of social media users from the humanitarian sector and limit our search to those users only. Communities can also be created based on hashtags. The result of the “tsunami” search is displayed below.

This result can be filtered further by gender, sentiment, number of twitter followers, urgent words (e.g., alert, help, asap), time period and location, for example. The platform can monitor and view posts in any language that is posted. In addition, PeopleBrowsr have their very own Kred score which quantifies the “credibility” of social media users. The scoring metrics for Kred scores is completely transparent and also community driven. “Kred is a transparent way to measure influence and outreach in social media. Kred generates unique scores for every domain of expertise. Regardless of follower count, a person is influential if their community is actively listening and engaging with their content.”

Using Kred, PeopleBrows can do influence analysis using Twitter across all languages. They’ve also added Facebook to Kred, but only as an opt in option.  PeopleBrowsr also has some great built-in and interactive data analytics tools. In addition, one can download a situation report in PDF and print that off if there’s a need to go offline.

What appeals to me the most is perhaps the full “drill-down” functionality of PeopleBrowsr’s data analytics tools. For example, I can drill down to the number of tweets per month that reference the word “tsunami” and drill down further per week and per day.

Moreover, I can sort through the individual tweets themselves based on specific filters and even access the underlying tweets complete with twitter handles, time-stamps, Kred scores, etc.

This latter feature would make it possible for the SBTF to copy & paste and map individual tweets on a live crisis map. In fact, the underlying data can be downloaded into a CSV file and added to a Google Spreadsheet for Mapsters to curate. Hopefully the Ushahidi team will also provide an option to upload CSVs to SwiftRiver so users can curate/filter pre-existing datasets as well as content generated live. What if you don’t have time to get on PeopleBrowsr and filter, download, etc? As part of their customer support, PeopleBrowsr will simply provide the data to you directly.

So what’s next? Marc and I are taking the following steps: Schedule online demo of PeopleBrowsr of the SBTF Core Team (they are for now the only members of the Digital Humanitarian Network with a dedicated and experienced Media Monitoring Team); SBTF pilots PeopleBrowsr for preparedness purposes; SBTF deploys  PeopleBrowsr during 2-3 official activations of the Digital Humanitarian Network; SBTF analyzes the added value of PeopleBrowsr for humanitarian response and provides expert feedback to PeopleBrowsr on how to improve the tool for humanitarian response.

From Crowdsourcing Crisis Information to Crowdseeding Conflict Zones (Updated)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Surprising Findings: Using Mobile Phones to Predict Population Displacement After Major Disasters

Rising concerns over the consequences of mass refugee flows during several crises in the late 1970’s is what prompted the United Nations (UN) to call for the establishment of early warning systems for the first time. “In 1978-79 for example, the United Nations and UNHCR were clearly overwhelmed by and unprepared for the mass influx of Indochinese refugees in South East Asia. The number of boat people washed onto the beaches there seriously challenged UNHCR’s capability to cope. One of the issues was the lack of advance information. The result was much human suffering, including many deaths. It took too long for emergency assistance by intergovernmental and non-governmental organizations to reach the sites” (Druke 2012 PDF).

Forty years later, my colleagues at Flowminder are using location data from mobile phones to nowcast and predict population displacement after major disasters. Focusing on the devastating 2010 Haiti earthquake, the team analyzed the movement of 1.9 million mobile users before and after the earthquake. Naturally, the Flowminder team expected that the mass exodus from Port-au-Prince would be rather challenging to predict. Surprisingly, however, the predictability of people’s movements remained high and even increased during the three-month period following the earthquake.

The team just released their findings in a peer-reviewed study entitled: “Predictability of population displacement after the 2010 Haiti earthquake” (PNAS 2012). As the analysis reveals, “the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds, as measured by where they spent Christmas and New Year holidays” (PNAS 2012).

For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought” (PNAS 2012). Intriguingly, the analysis also revealed the period of time that people in Port-au-Prince waited to leave the city (and then return) was “power-law distributed, both during normal days and after the earthquake, albeit with different exponents (PNAS 2012).” Clearly then, “[p]eople’s movements are highly influenced by their historic behavior and their social bonds, and this fact remained even after one of the most severe disasters in history” (PNAS 2012).

 

I wonder how this approach could be used in combination with crowdsourced satellite imagery analysis on the one hand and with Agent Based Models on the other. In terms of crowdsourcing, I have in mind the work carried out by the Standby Volunteer Task Force (SBTF) in partnership with UNHCR and Tomnod in Somalia last year. SBTF volunteers (“Mapsters”) tagged over a quarter million features that looked liked IDP shelters in under 120 hours, yielding a triangulated country of approximately 47,500 shelters.

In terms of Agent Based Models (ABMs), some colleagues and I  worked on “simulating population displacements following a crisis”  back in 2006 while at the Santa Fe Institute (SFI). We decided to use an Agent Based Model because the data on population movement was simply not within our reach. Moreover, we were particularly interested in modeling movements of ethnic populations after a political crisis and thus within the context of a politically charged environment.

So we included a preference for “safety in numbers” within the model. This parameter can easily be tweaked to reflect a preference for moving to locations that allow for the maintenance of social bonds as identified in the Flowminder study. The figure above lists all the parameters we used in our simple decision theoretic model.

The output below depicts the Agent Based Model in action. The multi-colored panels on the left depict the geographical location of ethnic groups at a certain period of time after the crisis escalates. The red panels on the right depict the underlying social networks and bonds that correspond to the geographic distribution just described. The main variable we played with was the size or magnitude of the sudden onset crisis to determine whether and how people might move differently around various ethnic enclaves. The study long with the results are available in this PDF.

In sum, it would be interesting to carry out Flowminder’s analysis in combination with crowdsourced satellite imagery analysis and live sensor data feeding into an Agent Base Model. Dissertation, anyone?

DeadUshahidi: Neither Dead Right Nor Dead Wrong

There’s a new Crowdmap in town called DeadUshahidi. The site argues that “Mapping doesn’t equal change. Using crowdsourcing tech like Ushahidi maps without laying the strategic and programmatic ground work is likely not going to work. And while we think great work has been done with crowdsourced reporting, there is an increasing number of maps that are set up with little thought as to why, who should care, and how the map leads to any changes.”

In some ways this project is stating the obvious, but the obvious sometimes needs repeating. As Ushahidi’s former Executive Director Ory Okolloh warned over two years ago: “Don’t get too jazzed up! Ushahidi is only 10% of solution.” My own doctoral research, which included a comparative analysis of Ushahidi’s use in Egypt and the Sudan, demonstrated that training, preparedness, outreach and strategic partnerships were instrumental. So I do appreciate DeadUshahidi’s constructive (and entertaining!) efforts to call attention to this issue and explain what makes a good crowd-sourced map.

At the same time, I think some of the assumptions behind this initiative need questioning. According to the project, maps with at least one of the following characteristics is added to the cemetery:

  • No one has submitted a report to your map in the last 12 months.
  • For time-bound events, like elections and disasters, the number of reports are so infinitesimally small (in relation to the number of the community the map is targeting) that the map never reached a point anywhere near relevance. (Our measure for elections is, for instance, # of submissions / # of registered voters > .0001).
  • The map was never actually started (no category descriptions, fewer than 10 reports). We call that a stillbirth.

Mapping doesn’t equal change, but why assume that every single digital map is launched to create change? Is every blog post written to create change? Is every Wikipedia article edit made to effect change? Every tweet? What was the impact of the last hard copy map you saw? Intention matters and impact cannot be measured without knowing the initial motivations behind a digital map, the intended theory of change and some kind of baseline to measure said change. Also, many digital maps are event-based and thus used for a limited period of time only. They may no longer receive new reports a year after the launch, but this doesn’t make it a “dead” map, simply a completed project. A few may even deserve to go to map heaven—how about a UshahidiHeaven crowdmap?

I’m also not entirely convinced by the argument that the number of reports per map has to cross a certain threshold for the crowdsourced map to be successful. A digital map of a neighborhood in Sydney with fewer than one hundred reports could very well have achieved the intended goal of the project. So again, without knowing or being able to reliably discern the motivations behind a digital map, it is rather farfetched to believe that one can assess whether a project was success-ful or not. Maybe most of the maps in the DeadUshahidi cemetery were never meant to live beyond a few days, weeks or months in the first place.

That said, I do think that one of the main challenges with Ushahidi/Crowdmap use is that the average number of reports per map is very, very low. Indeed, the vast majority of Crowdmaps are stillborn as a forthcoming study from Internews shows. Perhaps this long-tail effect shouldn’t be a surprise though. The costs of experimenting are zero and the easier the technology gets, the more flowers will bloom—or rather the more seeds become available. Whether these free and open source seeds actually get planted and grow into flowers (let alone lush eco-systems) is another issue and one dependent on a myriad of factors such as the experience of the “gardener”, the quality of the seeds, the timing and season, the conditions of the soil and climate, and the availability of other tools used for planting and cultivation.

Or perhaps a better analogy is photography. Thanks to Digital Cameras, we take zillions more pictures than we did just 5 years ago because each click is virtually free. We’re no longer limited to 24 or 36 pictures per roll of film, which first required one to buy said roll and later to pay for it again to be developed. As a result of digital cameras, one could argue that there are now a lot more bad quality (dead) pictures being uploaded everywhere. So what? Big deal. There is also more excellent amateur photography out there as well. What about other technologies and media? There are countless of “dead” Twitter accounts, WordPress blogs, Ning platforms, customized Google Maps, etc. Again, so what?

Neogeography is about democratizing map-making and user-generated maps. Naturally, there’s going to be learning and experimentation involved. So my blog post is not written in defense of Ushahidi/Crowdmap but rather in defense of all amateur digital mappers out there who are curious and just want to map whatever the heck they well please. In sum, and to return to the gardening analogy if I may, the more important question here is why the majority of (Usha)seeds aren’t planted or don’t grow, and what can be done about this in a pro-active manner. Is there something wrong with the seed? Do would-be gardeners simply need more gardening manuals? Or do they need more agile micro-tasking and data-mining tools? The upcoming Internews report goes a long way to explaining the why & what and TechChange’s course on Ushahidi may be one way to save some future maps from ending up in the DeadUshahidi cemetery prematurely.

Towards a Match.com for Economic Resilience in a Crisis-Stricken World

So that’s what he’s been up to! My good friend and mentor Ken Banks of Kiwanja fame has just launched a very interesting initiative entitled “Means of Exchange“. Ken wants to democratize opportunities for radical economic self-sufficiency and thus render local communities more resilient to exogenous shocks. “I’ve been taking an increasing interest in economic resilience,” writes Ken, “particularly how technology might help buffer local communities from global economic down-turns. Ironically, since I started my research the world has entered a period of growing economic uncertainty. The causes–although fascinating–don’t so much interest me, more the response at local, grassroots level.”

To say that Ken’s ideas directly resonate with my ideals would be a huge understatement. My iRevolution blog is currently in its fifth year of production and as the About page explains, “This blog features short thought pieces on how innovation and technology are revolutionizing the power of the individual through radical self-sufficiency, self-determination, independence, survival and resilience.” I’m incredibly excited by Ken’s new initiative. He quotes this excellent comment by Calvin Coolidge, which really hits home:

“We pay too little attention to the reserve power of the people to take care of themselves. We are too solicitous for government intervention, on the theory, first, that the people themselves are helpless, and second, that the government has superior capacity for action. Often times both of these conclusions are wrong.”

I have written many a blog post on this very people-centered notion as applied to crisis early warning and humanitarian response. Hence my pitch two years ago for a Match.com applied to humanitarian relief. Take this blog post, for example: “The Crowd is Always There: A Marketplace for Crowdsourcing Crisis Response.” But Ken is not just advocating for a “Match.com for Economic Resilience,” he is also building the infrastructure to make it happen: “A number of apps to support this work are planned for rollout during the year, with the first due for release in summer 2012.”

I can’t wait to see how Ken and his team take bartering, swapping, local exchange, vouchers, etc., to the next level. The results may have very interesting app-lications and implications for humanitarian response, and perhaps even serve as a grassroots response mechanism for the work of UN Global Pulse and others. To be sure, “the focus of most disaster management programmes is to deploy resources—physical and human—from outside the disaster zone. This activity can produce a delay in disaster mitigation and recovery efforts, and a consequent loss of human lives and economic resources” (Disasters 2012). So why not increase self-sufficiency at the community level and thereby build economic resilience?

I had very interesting conversations about just this question with several colleagues at The Fletcher School whilst doing my PhD a few years back. The result was this excellent USAID report on “Increasing the Financial Resilience of Disaster-Affected Populations.” Of course, disaster-affected communities have always been the real first-responders, and they have developed extensive coping mechanisms to manage various shocks. But why not build on these mechanisms and render them more agile with new mobile apps and technologies? After all: “Resilience is the capacity of the affected community to self-organize, learn from and vigorously recover from adverse situations stronger than it was before.”

This brings me to a conversation I had with my colleague Riley Crane whilst at SXSW 2012. He shared with me the incredible story of Chain 124, an amazing feat pulled off by the National Kidney Registry (NKR). One of the main challenges in kidney donation is not finding a compatible kidney even within the same family. Now take Family A and Family B each with a family member in need of a kidney but with no  relatives or friends with compatible kidneys to offer. But say one member of Family A has exactly the kind of kidney that a member in Family B requires and vice-versa. They’d be willing to swap. In a very simplified way, this is exactly what NKR was able to pull off in a chain of 30 swaps and 60 people across 7 hospitals in 11 states. This was made possible in large part thanks to innovations in computer matching.

So my question for Ken and team is whether these kinds of multiple, cascading computing-matched chains could also be developed for radical economic self-sufficiency and financial resilience “Means of Exchange” apps? Could such bartering and swapping chains occur across multiple products rather than just one? And finally, how can I help you in your cause?

Finally, A Decision-Support Platform for SMS Use in Disaster Response

Within weeks of the 2010 Haiti Earthquake, I published this blog post entitled “How to Royally Mess Up Disaster Response in Haiti.” A month later, I published another post on “Haiti and the Tyranny of Technology.” I also called for an SMS Code of Conduct as described here. Some of the needs and shortcomings expressed in these blog posts have finally been answered by InfoAsAid‘s excellent Message Library, “an online searchable database of messages that acts as a reference for those wanting to disseminate critical information to affected populations in an emergency.”

“If used in the correct way, the library should help improve communication with crisis-affected populations.” As my colleague Anahi Ayala explains with respect to the disaster response in Haiti,

“One of the main problem that emerged was not only the need to communicate but the need for a coordinated and homogeneous message to be delivered to the affected communities. The problem was posed by the fact that as agencies and organizations were growing in number and size, all of them were trying in different ways to deliver messages to the beneficiaries of aid, with the result of many messages, sometimes contradicting each other, delivered to many people, sometimes not the right receiver for that message.”

This platform can be used for both disaster response and preparedness. In the latter case, preparedness exercises can “Involve communities to identify threats and draft appropriate messages using the message library as a reference.” Organizations can also “Pre-test the messages with different segments of society (consider differences in gender, rural/urban, education levels, age) to ensure comprehension.” In terms of disaster response, the platform can be used to disseminate information on the “scale, nature and impact of the disaster (humanitarian news); Alerts about secondary disasters such as aftershocks, landslides or flooding; Messages about how to stay safe and mitigate risk in the face of anticipated threats.”

At PeaceTXT, we’re taking a very similar approach to SMS messaging. In our case, we are developing an SMS Library specifically for the purposes of changing recipients’ behaviors and perceptions vis-a-vis peace and conflict issues in Kenya. This shift towards a culture of preparedness is really important, both for disaster response and conflict prevention. We are currently organizing a series of focus groups with local communities to develop the content of our SMS Library. We plan to review this content in August for inclusion in the library. I very much look forward to scheduling a conference call between InfoAsAid and PeaceTXT in the coming months to share lessons learned thus far in the development of our respective message libraries.

For more on InfoAsAid’s absolutely critical resource, this short video provides a very good summary, including how sensitive messages are managed and how you can contribute SMS content to this very important service. Some serious thanks and praise are in order for InfoAsAid’s work. I do hope that the team at InfoAsAid will join us at the International Crisis Mappers Conference  (ICCM 2012) to share the latest on their excellent initiatives.

Evolution in Live Mapping: The 2012 Egyptian Presidential Elections

My doctoral dissertation compared the use of live mapping technology in Egypt and the Sudan during 2010. That year was the first time that Ushahidi was deployed in those two countries. So it is particularly interesting to see the technology used again in both countries in 2012. Sudanese activists are currently using the platform to map #SudanRevolts while Egyptian colleagues have just used the tool to monitor the recent elections in their country.

Analyzing the evolution of live mapping technology use in non-permissive environments ought to make for a very interesting piece of research (any takers?). In the case of Egypt, one could compare the use of the same technology and methods before and after the fall of Mubarak. In 2010, the project was called U-Shahid. This year, the initiative was branded as the “Egypt Elections Project.”

According to my colleagues in Cairo who managed the interactive map, “more than 15 trainers and 75 coordinators were trained to work in the ‘operation room’ supporting 2200 trained observers scattered all over Egypt. More than 17,000 reports, up to 25000 short messages were sent by the observers and shown on Ushahid’s interactive map. Although most reports received shown a minimum amount of serious violations, and most of them were indicating the success of the electoral process, our biggest joy was being able to monitor freely and to report the whole process with full transparency.”

Contrast this situation with how Egyptian activists struggled to keep their Ushahidi project alive under Mubarak in 2010. Last week, the team behind the current live map was actually interviewed by state television (picture above), which was formerly controlled by the old regime. Interestingly, the actual map is no longer the centerpiece of the project when compared to the U-Shahid deploy-ment. The team has included and integrated a lot more rich multimedia content in addition to data, statistics and trends analysis. Moreover, there appears to be a shift towards bounded crowdsourcing rather than open crowd-sourcing as far as election mapping projects go.

These two live mapping projects in Egypt and the Sudan are also getting relatively more traction than those in 2010. Some 17,000 reports were mapped in this year’s election project compared to 2,700 two years ago. Apparently, “millions of users logged into the [Egypt Project Elections] site to check the outcome of the electoral process,” compared to some 40,000 two years ago. Sudanese activists in Khartoum also appear to be far better organized and more agile at leverage social media channels to garner support for their movement than in 2010. Perhaps some of the hard lessons from those resistance efforts were learned.

This learning factor is key and relates to an earlier blog post I wrote on “Technology and Learning, Or Why the Wright Brothers Did Not Create the 747.” Question is: do repressive regimes learn faster or do social movements operate with more agile feedback loops? Indeed, perhaps the technology variable doesn’t matter the most. As I explained to Newsweek a while back, “It is the organiza-tional structure that will matter the most. Rigid structures are unable to adapt as quickly to a rapidly changing environment as a decentralized system. Ultimately, it is a battle of organizational theory.” In the case of Egypt and Sudan today, there’s no doubt that activists in both countries are better organized while the technologies themselves haven’t actually changed much since 2010. But better organization is a necessary, not sufficient, condition to catalyze positive social change and indirect forms of democracy.

Pierre Rosanvallon (2008) indentifies three channels whereby civil society can hold the state accountable during (and in between) elections, and independent of their results.

“The first refers to the various means whereby citizens (or, more accurately, organizations of citizens) are able to monitor and publicize the behavior of elected and appointed rulers; the second to their capacity to mobilize resistance to specific policies, either before or after they have been selected; the third to the trend toward ‘juridification’ of politics when individuals or social groups use the courts and, especially, jury trials to bring delinquent politicians to judgment.”

Live maps and crowdsourcing can be used to monitor and publicize the behavior of politicians. The capacity to mobilize resistance and bring officials to judgment may require a different set of strategies and technologies, however. Those who don’t realize this often leave behind a cemetery of dead maps.

Muḥammad ibn Mūsā al-Khwārizmī: An Update from the Qatar Computing Research Institute

I first heard of al-Khwārizmī in my ninth-grade computer science class at the International School of Vienna (AIS) back in 1993. Dr. Herman Prossinger who taught the course is exactly the kind of person one describes when answering the question: which teacher had the most impact on you while growing up? I wonder how many other 9th graders in the world had the good fortune of being taught computer science by a full-fledged professor with a PhD dissertation entitled “Isothermal Gas spheres in General Relativity Theory” (1976) and numerous peer-reviewed publications in top-tier scientific journals including Nature?

Muḥammad ibn Mūsā al-Khwārizmī was a brilliant mathematician & astronomer who spent his time as a scholar in the House of Wisdom in Baghdad (possibly the best name of any co-working space in history). “Al-Khwarithmi” was initially transliterated into Latin as Algoritmi. The manuscript above, for example, begins with “DIXIT algorizmi,” meaning “Says al-Khwārizmī.” And thus was born the world AlgorithmBut al-Khwārizmī’s fundamental contributions were not limited to the fields of mathematics and astronomy, he is also well praised for his important work on geography and cartography. Published in 833, his Kitāb ṣūrat al-Arḍ (Arabic: كتاب صورة الأرض) or “Book on the Appearance of the Earth” was a revised and corrected version of Ptolemy’s Geography. al-Khwārizmī’s book comprised an impressive list of 2,402 coordinates of cities and other geo-graphical features. The only surviving copy of the book can be found at Strasbourg University. I’m surprised the item has not yet been purchased by Qatar and relocated to Doha.

View of the bay from QCRI in Doha, Qatar.

This brings me to the Qatar (Foundation) Computing Research Institute (QCRI), which was almost called the al-Khwārizmī Computing Research Institute. I joined QCRI exactly two weeks ago as Director of Social Innovation. My first impression? QCRI is Doha’s “House of Whizzkids”. The team is young, dynamic, international and super smart. I’m already working on several exploratory research and development (R&D) projects that could potentially lead to initial prototypes by the end of the year. These have to do with the application of social computing and big data analysis for humanitarian response. So I’ve been in touch with several colleagues at the United Nations (UN) Office for the Coordination of Humanitarian Affairs (OCHA) to bounce these early ideas off and am thrilled that all responses thus far have been very positive.

My QCRI colleagues and I are also looking into collaborative platforms for “smart microtasking” which may be useful for the Digital Humanitarian Network. In addition, we’re just starting to explore potential solutions for quantifying veracity in social media, a rather non-trivial problem as Dr. Prossinger would often say with a sly smile in relation to NP-hard problems. In terms of partner-ship building, I will be in New York, DC and Boston next month for official meetings with the UN, World Bank and MIT to explore possible collaborations on specific projects. The team in Doha is particularly strong on big data analytics, social computing, data cleaning, machine learning and translation. In fact, most of the whizzkids here come from very impressive track records with Microsoft, Yahoo, Ivy Leagues, etc. So I’m excited by the potential.

View of Tornado Tower (purple lights) where QCRI is located.

The reason I’m not going into specifics vis-a-vis these early R&D efforts is not because I want to be secretive or elusive. Not at all. We’re still refining the ideas ourselves and simply want to manage expectations. There is a very strong and genuine interest within QCRI to contribute meaningfully to the humanitarian technology space. But we’re really just getting started, still hiring left, center and right, and we’ll be in R&D mode for a while. Plus, we don’t want to rush just for the sake of launching a new product. All too often, humanitarian technologies are developed without the benefit (and luxury) of advanced R&D. But if QCRI is going to help shape next-generation humanitarian technology solutions, we should do this in a way that is deliberate, cutting-edge and strategic. That is our comparative advantage.

In sum, the outcome of our R&D efforts may not always lead to a full-fledged prototype, but all the research and findings we carry out will definitely be shared publicly so we can move the field forward. We’re also committed to developing free and open source software as part of our prototyping efforts. Finally, we have no interest in re-inventing the wheel and far prefer working in partnerships than in isolation. So there we go, time to R&D  like al-Khwārizmī.