Framework

Humanitarian donors and organizations are increasingly championing innovation and the use of new technologies for humanitarian response. DfID, for example, is committed to using “innovative techniques and technologies more routinely in humanitarian response” (2011). In a more recent strategy paper, DfID confirmed that it would “continue to invest in new technologies” (2012). ALNAP’s important report on “The State of the Humanitarian System” documents the shift towards greater innovation, “with new funds and mechanisms designed to study and support innovation in humanitarian programming” (2012). A forthcoming land-mark study by OCHA makes the strongest case yet for the use and early adoption of new technologies for humanitarian response (2013).

These strategic policy documents are game-changers and pivotal to ushering in the next wave of humanitarian technology and innovation. That said, the reports are limited by the very fact that the authors are humanitarian professionals and thus not necessarily familiar with the field of advanced computing. The purpose of this post is therefore to set out a more detailed research framework for next generation humanitarian technology and innovation—one with a strong focus on information systems for crisis response and management.

In 2010, I wrote this piece on “The Humanitarian-Technology Divide and What To Do About It.” This divide became increasingly clear to me when I co-founded and co-directed the Harvard Humanitarian Initiative’s (HHI) Program on Crisis Mapping & Early Warning (2007-2009). So I co-founded the annual Inter-national CrisisMappers Conference series in 2009 and have continued to co-organize this unique, cross-disciplinary forum on humanitarian technology. The CrisisMappers Network also plays an important role in bridging the humanitarian and technology divide. My decision to join Ushahidi as Director of Crisis Mapping (2009-2012) was a strategic move to continue bridging the divide—and to do so from the technology side this time.

The same is true of my move to the Qatar Computing Research Institute (QCRI) at the Qatar Foundation. My experience at Ushahidi made me realize that serious expertise in Data Science is required to tackle the major challenges appearing on the horizon of humanitarian technology. Indeed, the key words missing from the DfID, ALNAP and OCHA innovation reports include: Data Science, Big Data Analytics, Artificial Intelligence, Machine Learning, Machine Translation and Human Computing. This current divide between the humanitarian and data science space needs to be bridged, which is precisely why I joined the Qatar Com-puting Research Institute as Director of Innovation; to develop and prototype the next generation of humanitarian technologies by working directly with experts in Data Science and Advanced Computing.

My efforts to bridge these communities also explains why I am co-organizing this year’s Workshop on “Social Web for Disaster Management” at the 2013 World Wide Web conference (WWW13). The WWW event series is one of the most prestigious conferences in the field of Advanced Computing. I have found that experts in this field are very interested and highly motivated to work on humanitarian technology challenges and crisis computing problems. As one of them recently told me: “We simply don’t know what projects or questions to prioritize or work on. We want questions, preferably hard questions, please!”

Yet the humanitarian innovation and technology reports cited above overlook the field of advanced computing. Their policy recommendations vis-a-vis future information systems for crisis response and management are vague at best. Yet one of the major challenges that the humanitarian sector faces is the rise of Big (Crisis) Data. I have already discussed this here, here and here, for example. The humanitarian community is woefully unprepared to deal with this tidal wave of user-generated crisis information. There are already more mobile phone sub-scriptions than people in 100+ countries. And fully 50% of the world’s population in developing countries will be using the Internet within the next 20 months—the current figure is 24%. Meanwhile, close to 250 million people were affected by disasters in 2010 alone. Since then, the number of new mobile phone subscrip-tions has increased by well over one billion, which means that disaster-affected communities today are increasingly likely to be digital communities as well.

In the Philippines, a country highly prone to “natural” disasters, 92% of Filipinos who access the web use Facebook. In early 2012, Filipinos sent an average of 2 billion text messages every day. When disaster strikes, some of these messages will contain information critical for situational awareness & rapid needs assess-ment. The innovation reports by DfID, ALNAP and OCHA emphasize time and time again that listening to local communities is a humanitarian imperative. As DfID notes, “there is a strong need to systematically involve beneficiaries in the collection and use of data to inform decision making. Currently the people directly affected by crises do not routinely have a voice, which makes it difficult for their needs be effectively addressed” (2012). But how exactly should we listen to millions of voices at once, let alone manage, verify and respond to these voices with potentially life-saving information? Over 20 million tweets were posted during Hurricane Sandy. 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, i.e., 2,000 tweets per second on average.

Of course, the volume and velocity of crisis information will vary from country to country and disaster to disaster. But the majority of humanitarian organizations do not have the technologies in place to handle smaller tidal waves either. Take the case of the recent Typhoon in the Philippines, for example. OCHA activated the Digital Humanitarian Network (DHN) to ask them to carry out a rapid damage assessment by analyzing the 20,000 tweets posted during the first 48 hours of Typhoon Pablo. In fact, one of the main reasons digital volunteer networks like the DHN and the Standby Volunteer Task Force (SBTF) exist is to provide humanitarian organizations with this kind of skilled surge capacity. But analyzing 20,000 tweets in 12 hours (mostly manually) is one thing, analyzing 20 million requires more than a few hundred dedicated volunteers. What’s more, we do not have the luxury of having months to carry out this analysis. Access to information is as important as access to food; and like food, information has a sell-by date.

We clearly need a research agenda to guide the development of next generation humanitarian technology. One such framework is proposed her. The Big (Crisis) Data challenge is composed of (at least) two major problems: (1) finding the needle in the haystack; (2) assessing the accuracy of that needle. In other words, identifying the signal in the noise and determining whether that signal is accurate. Both of these challenges are exacerbated by serious time con-straints. There are (at least) two ways too manage the Big Data challenge in real or near real-time: Human Computing and Artificial Intelligence. We know about these solutions because they have already been developed and used by other sectors and disciplines for several years now. In other words, our information problems are hardly as unique as we might think. Hence the importance of bridging the humanitarian and data science communities.

In sum, the Big Crisis Data challenge can be addressed using Human Computing (HC) and/or Artificial Intelligence (AI). Human Computing includes crowd-sourcing and microtasking. AI includes natural language processing and machine learning. A framework for next generation humanitarian technology and inno-vation must thus promote Research and Development (R&D) that apply these methodologies for humanitarian response. For example, Verily is a project that leverages HC for the verification of crowdsourced social media content generated during crises. In contrast, this here is an example of an AI approach to verification. The Standby Volunteer Task Force (SBTF) has used HC (micro-tasking) to analyze satellite imagery (Big Data) for humanitarian response. An-other novel HC approach to managing Big Data is the use of gaming, something called Playsourcing. AI for Disaster Response (AIDR) is an example of AI applied to humanitarian response. In many ways, though, AIDR combines AI with Human Computing, as does MatchApp. Such hybrid solutions should also be promoted as part of the R&D framework on next generation humanitarian technology.

There is of course more to humanitarian technology than information manage-ment alone. Related is the topic of Data Visualization, for example. There are also exciting innovations and developments in the use of drones or Unmanned Aerial Vehicles (UAVs), meshed mobile communication networks, hyper low-cost satellites, etc.. I am particularly interested in each of these areas will continue to blog about them. In the meantime, I very much welcome feedback on this post’s proposed research framework for humanitarian technology and innovation.

The digital activism and resistance witnessed in Iran go to the heart of my dissertation research, which asks whether the information revolution empowers coercive regimes at the expense of resistance movements or vice versa? Iran is one of my case studies for my upcoming field research in addition to Burma, Tunisia and Ukraine.

Introduction

There have been a number of excellent blog posts on the intersection between technology and resistance in Iran, and especially on the use of Twitter. The mainstream press is also awash with references to Twitter’s role. For example, Agence France Presse (AFP) recently cited my research in this piece entitled “Twitter Streams Break Iran News Dam.”

However, what I haven’t seen in the blogosphere and mainstream press is the application of an analytical and theoretical framework to place Twitter’s use in Iran into context.

For example, just how important is/was Twitter’s role vis-a-vis the mobilization and organization of anti-government protests in Iran? We can draw on anecdotes here and there but this process is devoid of any applied social science methodology.

This post seeks to shed light on how, when and why information and communication technologies (ICTs) are used by resistance movements in repressive environments. The framework I draw on (summarized below) is informed by Kelly Garrett’s excellent publication on “Protest in an Information Society: A Review of the Literature on Social Movements and New ICTs” (2006).

The framework seeks to “explain the emergence, development and outcomes of social movements by addressing three interrelated factors: mobilizing structures, opportunity structures and framing processes” within the context of ICTs. (The figure below is excerpted from my dissertation, hence the figure 4 reference).

Mobilizing Structures are the mechanisms that facilitate organization and collective action. These include social structures and tactical repertoires.

Opportunity Structures are conditions that favor social movement activity. For example, these include factors such as the state’s capacity and propensity for repression.

Framing Processes are “strategic attempts to craft, disseminate, and contest the language and narratives used to describe a movement.”

These three factors should be further disaggregated to facilitate analysis. For example, mobilizing structures can be divided into categories susceptible to the impact of ICTs:

Participation levels (recruitment);
Contentious activity;
Organizational issues.

These sub-indicators are still to broad, however. Take, for example, participation levels; what is participation a function of? What underlying mechanisms are facilitated or constrained by the wider availability and use of ICTs? Participation levels may change as a function of three factors:

Reduction of participation costs;
Promotion of collective identity;
Creation of community.

These activities are of course not mutually exclusive but often interdependent. In any case, taking the analysis of ICTs in repressive environments to the tactical level facilitates the social science methodology of process tracing.

Application

We can apply the above framework to test a number of hypotheses regarding Twitter’s use in Iran. Take Mobilizing Structures, for example. The following hypothesis could be formulated.

Hypothesis 1: The availability of Twitter in Iran increased participation levels, contentious activity and organizational activity.

Using process tracing and the above framework, one could test hypothesis 1 as follows:

These causal chains, or “micro theories,” are posited with the “⎥” marker to signify that the causal relationship is contended. The direction of the arrows above reflects the theoretical narratives extracted from the theoretical framework presented above. Note that the above “micro” theories are general and not necessarily reflective of Twitter’s use in Iran.

Iran Case Study

When the arrows are tallied, the results suggest the following general theory: there is a direct and positive relationship between the impact of Twitter and the incidents of protests and riots. The next step is to test these “micro theories” in the context of Iran by actually “weighting” the arrows. And of course, to do so comparatively by testing the use of Twitter relative to the use of mobile phones and the Internet. Furthermore, the results of this hypothesis testing should be compared to those for Opportunity Structures and Framing Processes.

I plan to carry out field research to qualitatively test these hypotheses once the first phase of my dissertation is completed. The first phase is a large-N quantitative study to determine whether increasing access to ICTs in repressive regimes is a statistically significant predictor of anti-government protests.

Patrick Philippe Meier

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