Tag Archives: curation

GDACSmobile: Disaster Responders Turn to Bounded Crowdsourcing

Posted on March 27, 2013 | 6 comments

GDACS, the Global Disaster Alert and Coordination System, sparked my interest in technology and disaster response when it was first launched back in 2004, which is why I’ve referred to GDACS in multiple blog posts since. This near real-time, multi-hazard monitoring platform is a joint initiative between the UN’s Office for the Coordination of Humanitarian Affairs (OCHA) and the European Commission (EC). GDACS serves to consolidate and improve the dissemination of crisis-related information including rapid mathematical analyses of expected disaster impact. The resulting risk information is distributed via Web and auto-mated email, fax and SMS alerts.

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I recently had the pleasure of connecting with two new colleagues, Daniel Link and Adam Widera, who are researchers at the University of Muenster’s European Research Center for Information Systems (ERCIS). Daniel and Adam have been working on GDACSmobile, a smartphone app that was initially developed to extend the reach of the GDACS portal. This project originates from a student project supervised by Daniel, Adam along with the Chair of the Center Bernd Hellingrath in cooperation with both Tom de Groeve from the Joint Research Center (JRC) and Minu Kumar Limbu, who is now with UNICEF Kenya.

GDACSmobile is intended for use by disaster responders and the general public, allowing for a combined crowdsourcing and “bounded crowdsourcing” approach to data collection and curation. This bounded approach was a deliberate design feature for GDACSmobile from the outset. I coined the term “bounded crowd-sourcing” four years ago (see this blog post from 2009). The “bounded crowd-sourcing” approach uses “snowball sampling” to grow a crowd of trusted reporters for the collection of crisis information. For example, one invites 5 (or more) trusted local reports to collect relevant information and subsequently ask each of these to invite 5 additional reporters who they fully trust; And so on, and so forth. I’m thrilled to see this term applied in practical applications such GDACSmobile. For more on this approach, please see these blog posts.

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GDACSmobile, which operates on all major mobile smartphones, uses a delibera-tely minimalist approach to situation reporting and can be used to collect info-rmation (via text & image) while offline. The collected data is then automatically transmitted when a connection becomes available. Users can also view & filter data via map view and in list form. Daniel and Adam are considering the addition of an icon-based data-entry interface instead of text-based data-entry since the latter is more cumbersome & time-consuming.

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Meanwhile, the server side of GDACSmobile facilitates administrative tasks such as the curation of data submitted by app users and shared on Twitter. Other social media platforms may be added in the future, such as Flickr, to retrieve relevant pictures from disaster-affected areas (similar to GeoFeedia). The server-side moderation feature is used to ensure high data quality standards. But the ERCIS researchers are also open to computational solutions, which is one reason GDACSmobile is not a ‘data island’ and why other systems for computational analysis, microtasking etc., can be used to process the same dataset. The server also “offers a variety of JSON services to allow ‘foreign’ systems to access the data. […] SQL queries can also be used with admin access to the server, and it would be very possible to export tables to spreadsheets […].” 

I very much look forward to following GDACSmobile’s progress. Since Daniel and Adam have designed their app to be open and are also themselves open to con-sidering computational solutions, I have already begun to discuss with them our AIDR project (Artificial Intelligence for Disaster Response) project at the Qatar Computing Research Institute (QCRI). I believe that making the ADIR-GDACS interoperable would make a whole lot of sense. Until then, if you’re going to this year’s International Conference on Information Systems for Crisis Response and Management (ISCRAM 2013) in May, then be sure to participate in the workshop (PDF) that Daniel and Adam are running there. The side-event will present the state of the art and future trends of rapid assessment tools to stimulate a conver-sation on current solutions and developments in mobile tech-nologies for post-disaster data analytics and situational awareness. My colleague Dr. Imran Muhammad from QCRI will also be there to present findings from our crisis computing research, so I highly recommend connecting with him.

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Posted in Crowdsourcing, Humanitarian Technologies, Social Media

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CrisisTracker: Collaborative Social Media Analysis For Disaster Response

Posted on July 30, 2012 | 8 comments

I just had the pleasure of speaking with my new colleague Jakob Rogstadius from Madeira Interactive Technologies Institute (Madeira-TTI). Jakob is working on CrisisTracker, a very interesting platform designed to facilitate collaborative social media analysis for disaster response. The rationale for CrisisTracker is the same one behind Ushahidi’s SwiftRiver project and could be hugely helpful for crisis mapping projects carried out by the Standby Volunteer Task Force (SBTF).

From the CrisisTracker website:

“During large-scale complex crises such as the Haiti earthquake, the Indian Ocean tsunami and the Arab Spring, social media has emerged as a source of timely and detailed reports regarding important events. However, indivi-dual disaster responders, government officials or citizens who wish to access this vast knowledge base are met with a torrent of information that quickly results in information overload. Without a way to organize and navigate the reports, important details are easily overlooked and it is challenging to use the data to get an overview of the situation as a whole.”

We (Madeira University, University of Oulu and IBM Research) believe that volunteers around the world would be willing to assist hard-pressed decision makers with information management, if the tools were available. With this vision in mind, we have developed Crisis-Tracker.”

Like SwiftRiver, CrisisTracker combines some automated clustering of content with the crowdsourced curation of said content for further filtering. “Any user of the system can directly contribute tags that make it easier for other users to retrieve information and explore stories by similarity. In addition, users of the system can influence how tweets are grouped into stories.” Stories can be filtered by Report Category, Keywords, Named Entities, Time and Location. CrisisTracker also allows for simple geo-fencing to capture and list only those Tweets displayed on a given map.

Geolocation, Report Categories and Named Entities are all generated manually. The clustering of reports into stories is done automatically using keyword frequencies. So if keyword dictionaries exist for other languages, the platform could be used in these other languages as well. The result is a list of clustered Tweets displayed below the map, with the most popular cluster at the top.

Clicking on an entry like the row in red above opens up a new page, like the one below. This page lists a group of tweets that all discuss the same specific event, in this case an explosion in Syria’s capital.

What is particularly helpful about this setup is the meta-data displayed for this story or event: the number of people who tweeted about the story, the number of tweets about the story, the first day/time the story was shared on twitter. In addition, the first tweet to report the story is listed along, which is very helpful. This list can be ranked according to “Size” which is a figure that reflects the minimum number of original tweets and the number of Twitter users who shared these tweets. This is a particularly useful metric (and way to deal with spammers). Users also have the option of listing the first 50 tweets that referenced the story.

As you may be able to tell from the “Hide Story” and “Remove” buttons on the righthand-side of the display above, each clustered story and indeed tweet can be hidden or removed if not relevant. This is where crowdsourced curation comes in. In addition, CrisisTracker enable users to geo-tag and categorize each tweets according to report type (e.g., Violence, Deaths, Request/Need, etc.), general keywords (e.g., #assad, #blasts, etc.) and named entities. Note the the keywords can be removed and more high-quality tags can be added or crowdsourced by users as well (see below).

CrisisTracker also suggests related stories that may be of interest to the user based on the initial clustering and filtering—assisted manual clustering. In addition, the platform’s API means that the data can then be exported in XML using a simple parser. So interoperability with platforms like Ushahidi’s would be possible. After our call, Jakob added a link on each story page in the system (a small XML icon below the related stories) to get the story in XML format. Any other system can now take this URL and parse the story into its own native format. Jakob is also looking to build a number of extensions to CrisisTracker and a “Share with Ushahidi” button may be one such future extension. Crisis-Tracker is basically Jakob’s core PhD project, which is very cool, so he’ll be working on this for at least one more year.

In sum, this could very well be the platform that many of us in the crisis mapping space have been waiting for. As I wrote in February 2012, turning the Twitter-sphere “into real-time shared awareness will require that our filtering and curation platforms become more automated and collaborative. I believe the key is thus to combine automated solutions with real-time collaborative crowd-sourcing tools—that is, platforms that enable crowds to collaboratively filter and curate real-time information, in real-time. Right now, when we comb through Twitter, for example, we do so on our own, sitting behind our laptop, isolated from others who may be seeking to filter the exact same type of content. We need to develop free and open source platforms that allow for the distributed-but-networked, crowdsourced filtering and curation of information in order to democratize the sense-making of the firehose.”

Actually, I’ve been advocating for this approach since early 2009. So I’m really excited about Jakob’s project. We’ll be partnering with him and the Standby Volunteer Task Force (SBTF) in September 2012 to test the platform and provide him with expert feedback on how to further streamline the tool for collaborative social media analysis and crisis mapping. Jakob is also looking for domain experts to help on this study. In the meantime, I’ve invited Jakob to present Crisis-Tracker at the 2012 CrisisMappers Conference in Washington DC and very much hope he can join us to demo his tool to us in person. In the meantime, the video above provides an excellent overview of CrisisTracker, as does the project website. Finally, the project is also open source and available on Github here.

Epilogue: The main problem with CrisisTracker is that it is still too manual; it does not include any machine learning & artificial intelligence features; and has only focused on Syria. This may explain why it has not gained traction in the humanitarian space so far.

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Posted in Big Data, Crisis Mapping, Crowdsourcing, Humanitarian Technologies, Social Computing, Social Media

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Some Thoughts on Real-Time Awareness for Tech@State

Posted on February 2, 2012 | 6 comments

I’ve been invited to present at Tech@State in Washington DC to share some thoughts on the future of real-time awareness. So I thought I’d use my blog to brainstorm and invite feedback from iRevolution readers. The organizers of the event have shared the following questions with me as a way to guide the conver-sation: Where is all of this headed?  What will social media look like in five to ten years and what will we do with all of the data? Knowing that the data stream can only increase in size, what can we do now to prepare and prevent being over-whelmed by the sheer volume of data?

These are big, open-ended questions, and I will only have 5 minutes to share some preliminary thoughts. I shall thus focus on how time-critical crowdsourcing can yield real-time awareness and expand from there.

Two years ago, my good friend and colleague Riley Crane won DARPA’s $40,000 Red Balloon Competition. His team at MIT found the location of 10 weather balloons hidden across the continental US in under 9 hours. The US covers more than 3.7 million square miles and the balloons were barely 8 feet wide. This was truly a needle-in-the-haystack kind of challenge. So how did they do it? They used crowdsourcing and leveraged social media—Twitter in particular—by using a “recursive incentive mechanism” to recruit thousands of volunteers to the cause. This mechanism would basically reward individual participants financially based on how important their contributions were to the location of one or more balloons. The result? Real-time, networked awareness.

Around the same time that Riley and his team celebrated their victory at MIT, another novel crowdsourcing initiative was taking place just a few miles away at The Fletcher School. Hundreds of students were busy combing through social and mainstream media channels for actionable and mappable information on Haiti following the devastating earthquake that had struck Port-au-Prince. This content was then mapped on the Ushahidi-Haiti Crisis Map, providing real-time situational awareness to first responders like the US Coast Guard and US Marine Corps. At the same time, hundreds of volunteers from the Haitian Diaspora were busy translating and geo-coding tens of thousands of text messages from disaster-affected communities in Haiti who were texting in their location & most urgent needs to a dedicated SMS short code. Fletcher School students filtered and mapped the most urgent and actionable of these text messages as well.

One year after Haiti, the United Nation’s Office for the Coordination of Humanitarian Affairs (OCHA) asked the Standby Volunteer Task Force (SBTF) , a global network of 700+ volunteers, for a real-time map of crowdsourced social media information on Libya in order to improve their own situational awareness. Thus was born the Libya Crisis Map.

The result? The Head of OCHA’s Information Services Section at the time sent an email to SBTF volunteers to commend them for their novel efforts. In this email, he wrote:

“Your efforts at tackling a difficult problem have definitely reduced the information overload; sorting through the multitude of signals on the crisis is no easy task. The Task Force has given us an output that is manageable and digestible, which in turn contributes to better situational awareness and decision making.”

These three examples from the US, Haiti and Libya demonstrate what is already possible with time-critical crowdsourcing and social media. So where is all this headed? You may have noted from each of these examples that their success relied on the individual actions of hundreds and sometimes thousands of volunteers. This is primarily because automated solutions to filter and curate the data stream are not yet available (or rather accessible) to the wider public. Indeed, these solutions tend to be proprietary, expensive and/or classified. I thus expect to see free and open source solutions crop up in the near future; solutions that will radically democratize the tools needed to gain shared, real-time awareness.

But automated natural language processing (NLP) and machine learning alone are not likely to succeed, in my opinion. The data stream is actually not a stream, it is a massive torent of non-indexed information, a 24-hour global firehose of real-time, distributed multi-media data that continues to outpace our ability to produce actionable intelligence from this torrential downpour of 0’s and 1’s. To turn this data tsunami into real-time shared awareness will require that our filtering and curation platforms become more automated and collaborative. I believe the key is thus to combine automated solutions with real-time collabora-tive crowdsourcing tools—that is, platforms that enable crowds to collaboratively filter and curate real-time information, in real-time.

Right now, when we comb through Twitter, for example, we do so on our own, sitting behind our laptop, isolated from others who may be seeking to filter the exact same type of content. We need to develop free and open source platforms that allow for the distributed-but-networked, crowdsourced filtering and curation of information in order to democratize the sense-making of the firehose. Only then will the wider public be able to win the equivalent of Red Balloon competitions without needing $40,000 or a degree from MIT.

I’d love to get feedback from readers about what other compelling cases or arguments I should bring up in my presentation tomorrow. So feel free to post some suggestions in the comments section below. Thank you!

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

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