Tag Archives: University

Data Science for Social Good and Humanitarian Action

Posted on April 22, 2013 | 5 Comments

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

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

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

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Posted in Big Data, Social Computing

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Crisis Tweets: Natural Language Processing to the Rescue?

Posted on July 23, 2012 | 1 Comment

My colleagues at the University of Colorado, Boulder, have been doing some very interesting applied research on automatically extracting “situational awareness” from tweets generated during crises. As is increasingly recognized by many in the humanitarian space, Twitter can at times be an important source of relevant information. The challenge is to make sense of a potentially massive number of crisis tweets in near real-time to turn this information into situational awareness.

Using Natural Language Processing (NLP) and Machine Learning (ML), Colorado colleagues have developed a “suite of classifiers to differentiate tweets across several dimensions: subjectivity, personal or impersonal style, and linguistic register (formal or informal style).” They suggest that tweets contributing to situational awareness are likely to be “written in a style that is objective, impersonal, and formal; therefore, the identification of subjectivity, personal style and formal register could provide useful features for extracting tweets that contain tactical information.” To explore this hypothesis, they studied the follow four crisis events: the North American Red River floods of 2009 and 2010, the 2009 Oklahoma grassfires, and the 2010 Haiti earthquake.

The findings of this study were presented at the Association for the Advancement of Artificial Intelligence. The team from Colorado demonstrated that their system, which automatically classifies Tweets that contribute to situational awareness, works particularly well when analyzing ”low-level linguistic features,” i.e., word-frequencies and key-word search. Their analysis also showed that “linguistically-motivated features including subjectivity, personal/impersonal style, and register substantially improve system performance.” In sum, “these results suggest that identifying key features of user behavior can aid in predicting whether an individual tweet will contain tactical information. In demonstrating a link between situational awareness and other markable characteristics of Twitter communication, we not only enrich our classification model, we also enhance our perspective of the space of information disseminated during mass emergency.”

The paper, entitled: “Natural Language Processing to the Rescue? Extracting ‘Situational Awareness’ Tweets During Mass Emergency,” details the findings above and is available here. The study was authored by Sudha Verma, Sarah Vieweg, William J. Corvey, Leysia Palen, James H. Martin, Martha Palmer, Aaron Schram and Kenneth M. Anderson.

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Posted in Big Data, Crowdsourcing, Social Computing, Social Media

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Disaster Response, Self-Organization and Resilience: Shocking Insights from the Haiti Humanitarian Assistance Evaluation

Posted on May 23, 2012 | 6 Comments

Tulane University and the State University of Haiti just released a rather damming evaluation of the humanitarian response to the 2010 earthquake that struck Haiti on January 12th. The comprehensive assessment, which takes a participatory approach and applies a novel resilience framework, finds that despite several billion dollars in “aid”, humanitarian assistance did not make a detectable contribution to the resilience of the Haitian population and in some cases increased certain communities’ vulnerability and even caused harm. Welcome to supply-side humanitarian assistance directed by external actors.

“All we need is information. Why can’t we get information?” A quote taken from one of many focus groups conducted by the evaluators. “There was little to no information exchange between the international community tasked with humanitarian response and the Haitian NGOs, civil society or affected persons / communities themselves.” Information is critical for effective humanitarian assistance, which should include two objectives: “preventing excess mortality and human suffering in the immediate, and in the longer term, improving the community’s ability to respond to potential future shocks.” This longer term objective thus focuses on resilience, which the evaluation team defines as follows:

“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 link between resilience and capacity for self-organization is truly profound and incredibly important. To be sure, the evaluation reveals that “the humani-tarian response frequently undermined the capacity of Haitian individuals and organizations.” This completely violates the Hippocratic Oath of Do No Harm. The evaluators thus “promote the attainment of self-sufficiency, rather than the ongoing dependency on standard humanitarian assistance.” Indeed, “focus groups indicated that solutions to help people help themselves were desired.”

I find it particularly telling that many aid organizations interviewed for this assessment were reluctant to assist the evaluators in fully capturing and analyzing resource flows, which are critical for impact evaluation. “The lack of transparency in program dispersal of resources was a major constraint in our research of effective program evaluation.” To this end, the evaluation team argue that “by strengthening Haitian institutions’ ability to monitor and evaluate, Haitians will more easily be able to track and monitor international efforts.”

I completely disagree with this remedy. The institutions are part of the problem, and besides, institution-building takes years if not decades. To assume there is even political will and the resources for such efforts is at best misguided. If resilience is about strengthening the capacity of affected communities to self-organize, then I would focus on just that, applying existing technologies and processes that both catalyze and facilitate demand-side, people-centered self-organization. My previous blog post on “Technology and Building Resilient Societies to Mitigate the Impact of Disasters” elaborates on this point.

In sum, “resilience is the critical link between disaster and development; monitoring it will ensure that relief efforts are supporting, and not eroding, household and community capabilities.” This explains why crowdsourcing and data mining efforts like those of Ushahidi, HealthMap and UN Global Pulse are important for disaster response, self-organization and resilience.

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Posted in Crowdsourcing, Humanitarian Tech, Ushahidi

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