Tag Archives: red

Verily: Crowdsourced Verification for Disaster Response

Posted on February 19, 2013 | 18 comments

Social media is increasingly used for communicating during crises. This rise in Big (Crisis) Data means that finding the proverbial needle in the growing haystack of information is becoming a major challenge. Social media use during Hurricane Sandy produced a “haystack” of half-a-million Instagram photos and 20 million tweets. But which of these were actually relevant for disaster response and could they have been detected in near real-time? The purpose of QCRI’s experimental Twitter Dashboard for Disaster Response project is to answer this question. But what about the credibility of the needles in the info-stack?

10-Red-Balloons

To answer this question, our Crisis Computing Team at QCRI has partnered with the Social Computing & Artificial Intelligence Lab at the Masdar Institute of Science and Technology. This applied research project began with a series of conversations in mid-2012 about DARPA’s Red Balloon Challenge. This challenge posted in 2009 offered $40K to the individual or team that could find the correct location of 10 red weather balloons discretely placed across the continental United States, an area covering well over 3 million square miles (8 million square kilometers). My friend Riley Crane at MIT spearheaded the team that won the challenge in 8 hours and 52 minutes by using social media.

Riley and I connected right after the Haiti Earthquake to start exploring how we might apply his team’s winning strategy to disaster response. But we were pulled in different directions due to PhD & post-doc obligations and start-up’s. Thank-fully, however, Riley’s colleague Iyad Rahwan got in touch with me to continue these conversations when I joined QCRI. Iyad is now at the Masdar Institute. We’re collaborating with him and his students to apply collective intelligence insights from the balloon to address the problem of false or misleading content shared on social media during  disasters.

Screen Shot 2013-02-16 at 2.26.41 AM

If 10 balloons planted across 3 million square miles can be found in under 9 hours, then surely the answer to the question “Did Hurricane Sandy really flood this McDonald’s in Virginia?” can be found in under 9 minutes given that  Virginia is 98% smaller than the “haystack” of the continental US. Moreover, the location of the restaurant would already be known or easily findable. The picture below, which made the rounds on social media during the hurricane is in reality part of an art exhibition produced in 2009. One remarkable aspect of the social media response to Hurricane Sandy was how quickly false information got debunked and exposed as false—not only by one good (digital) Samaritan, but by several.

SandyFake

Having access to accurate information during a crisis leads to more targeted self-organized efforts at the grassroots level. Accurate information is also important for emergency response professionals. The verification efforts during Sandy were invaluable but disjointed and confined to the efforts of a select few individuals. What if thousands could be connected and mobilized to cross-reference and verify suspicious content shared on social media during a disaster?

Say an earthquake struck Santiago, Chile a few minutes ago and contradictory reports begin to circulate on social media that the bridge below may have been destroyed. Determining whether transportation infrastructure is still useable has important consequences for managing the logistics of a disaster response opera-tion. So what if instead of crowdsourcing the correct location of  balloons across an entire country, one could crowdsource the collection of evidence in just one city struck by a disaster to determine whether said bridge had actually been destroyed in a matter of minutes?

santiagobridge

To answer these questions, QCRI and Masdar have launched an experimental  platform called Verily. We are applying best practices in time-critical crowd-sourcing coupled with gamification and reputation mechanisms to leverage the good will of (hopefully) thousands of digital Samaritans during disasters. This is experimental research, which means it may very well not succeed as envisioned. But that is a luxury we have at QCRI—to innovate next-generation humanitarian technologies via targeted iteration and experimentation. For more on this project, our concept paper is available as a Google Doc here. We invite feedback and welcome collaborators.

In the meantime, we are exploring the possibility of integrating the InformCam mobile application as part of Verily. InformaCam adds important metadata to images and videos taken by eyewitnesses. “The metadata includes information like the user’s current GPS coordinates, altitude, compass bearing, light meter readings, the signatures of neighboring devices, cell towers, and wifi net-works; and serves to shed light on the exact circumstances and contexts under which the digital image was taken.” We are also talking to our partners at MIT’s Computer Science & Artificial Intelligence Lab in Boston about other mobile solutions that may facilitate the use of Verily.

Again, this is purely experimental and applied research at this point. We hope to have an update on our progress in the coming months.

Bio

See also:

  •  Crowdsourcing Critical Thinking to Verify Social Media During Crises [Link]
  •  Using Crowdsourcing to Counter Rumors on Social Media [Link]

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Posted in Big Data, Crowdsourcing, Humanitarian Technologies, Information Forensics, Social Media

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Six Degrees of Separation: Implications for Verifying Social Media

Posted on September 18, 2012 | 6 comments

The Economist recently published this insightful article entitled” Six Degrees of Mobilisation: To what extent can social networking make it easier to find people and solve real-world problems?” The notion, six degrees of separation, comes from Stanley Milgram’s experiment in the 1960s which found that there were, on average, six degrees of separation between any two people in the US. Last year, Facebook found that users on the social network were separated by an average of 4.7 hops. The Economist thus asks the following, fascinating question:

“Can this be used to solve real-world problems, by taking advantage of the talents and connections of one’s friends, and their friends? That is the aim of a new field known as social mobilisation, which treats the population as a distributed knowledge resource which can be tapped using modern technology.”

The article refers to DARPA’s Red Balloon Challenge, which I already blogged about here: “Time-Critical Crowdsourcing for Social Mobilization and Crowd-Solving.”  The Economist also references DARPA’s TagChallenge. In both cases, the winning teams leveraged social media using crowdsourcing and clever incentive mechanisms. Can this approach also be used to verify social media content during a crisis?

This new study on disasters suggests that the “degrees of separation” between any two organizations in the field is 5. So if the location of red balloons and individuals can be crowdsourced surprisingly quickly, then can the evidence necessary to verify social media content during a disaster be collected as rapidly and reliably? If we are only separated by four-to-six degrees, then this would imply that it only takes that many hops to find someone connected to me (albeit indirectly) who could potentially confirm or disprove the authenticity of a particularly piece of information. This approach was used very successfully in Kyrgyzstan a couple years ago. Can we develop a platform to facilitate this process? And if so, what design features (e.g., gamification) are necessary to mobilize participants and make this tool a success?

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Posted in Crowdsourcing, Information Forensics, Social Computing, Social Media

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Behind the Scenes: The Digital Operations Center of the American Red Cross

Posted on April 17, 2012 | 13 comments

The Digital Operations Center at the American Red Cross is an important and exciting development. I recently sat down with Wendy Harman to learn more about the initiative and to exchange some lessons learned in this new world of digital  humanitarians. One common challenge in emergency response is scaling. The American Red Cross cannot be everywhere at the same time—and that includes being on social media. More than 4,000 tweets reference the Red Cross on an average day, a figure that skyrockets during disasters. And when crises strike, so does Big Data. The Digital Operations Center is one response to this scaling challenge.

Sponsored by Dell, the Center uses customized software produced by Radian 6 to monitor and analyze social media in real-time. The Center itself sits three people who have access to six customized screens that relate relevant information drawn from various social media channels. The first screen below depicts some of key topical areas that the Red Cross monitors, e.g., references to the American Red Cross, Storms in 2012, and Delivery Services.

Circle sizes in the first screen depict the volume of references related to that topic area. The color coding (red, green and beige) relates to sentiment analysis (beige being neutral). The dashboard with the “speed dials” right underneath the first screen provides more details on the sentiment analysis.

Lets take a closer look at the circles from the first screen. The dots “orbiting” the central icon relate to the categories of key words that the Radian 6 platform parses. You can click on these orbiting dots to “drill down” and view the individual key words that make up that specific category. This circles screen gets updated in near real-time and draws on data from Twitter, Facebook, YouTube, Flickr and blogs. (Note that the distance between the orbiting dots and the center does not represent anything).

An operations center would of course not be complete without a map, so the Red Cross uses two screens to visualize different data on two heat maps. The one below depicts references made on social media platforms vis-a-vis storms that have occurred during the past 3 days.

The screen below the map highlights the bio’s of 50 individual twitter users who have made references to the storms. All this data gets generated from the “Engagement Console” pictured below. The purpose of this web-based tool, which looks a lot like Tweetdeck, is to enable the Red Cross to customize the specific types of information they’re looking form, and to respond accordingly.

Lets look at the Consul more closely. In the Workflow section on the left, users decide what types of tags they’re looking for and can also filter by priority level. They can also specify the type of sentiment they’re looking, e.g., negative feelings vis-a-vis a particular issue. In addition, they can take certain actions in response to each information item. For example, they can reply to a tweet, a Facebook status update, or a blog post; and they can do this directly from the engagement consul. Based on the license that the Red Cross users, up to 25 of their team members can access the Consul and collaborate in real-time when processing the various tweets and Facebook updates.

The Consul also allows users to create customized timelines, charts and wordl graphics to better understand trends changing over time in the social media space. To fully leverage this social media monitoring platform, Wendy and team are also launching a digital volunteers program. The goal is for these volunteers to eventually become the prime users of the Radian platform and to filter the bulk of relevant information in the social media space. This would considerably lighten the load for existing staff. In other words, the volunteer program would help the American Red Cross scale in the social media world we live in.

Wendy plans to set up a dedicated 2-hour training for individuals who want to volunteer online in support of the Digital Operations Center. These trainings will be carried out via Webex and will also be available to existing Red Cross staff.


As  argued in this previous blog post, the launch of this Digital Operations Center is further evidence that the humanitarian space is ready for innovation and that some technology companies are starting to think about how their solutions might be applied for humanitarian purposes. Indeed, it was Dell that first approached the Red Cross with an expressed interest in contributing to the organization’s efforts in disaster response. The initiative also demonstrates that combining automated natural language processing solutions with a digital volunteer net-work seems to be a winning strategy, at least for now.

After listening to Wendy describe the various tools she and her colleagues use as part of the Operations Center, I began to wonder whether these types of tools will eventually become free and easy enough for one person to be her very own operations center. I suppose only time will tell. Until then, I look forward to following the Center’s progress and hope it inspires other emergency response organizations to adopt similar solutions.

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Posted in Crisis Mapping, Crowdsourcing, Early Warning, 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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Time-Critical Crowdsourcing for Social Mobilization and Crowd-Solving

Posted on November 6, 2011 | 15 comments

My good friend Riley Crane just co-authored a very interesting study entitled “Time-Critical Social Mobilization” in the peer-reviewed journal Science. Riley spearheaded the team at MIT that won the DARPA Red Balloon competition last year. His team found the locations of all 10 weather balloons hidden around the continental US in under 9 hours. While we were already discussing alternative approaches to crowdsourcing for social impact before the competition, the approach he designed to win the competition certainly gave us a whole lot more to talk about given the work I’d been doing on crowd sourcing crisis information and near real-time crisis mapping.

Crowd-solving non-trivial problems in quasi real-time poses two important challenges. A very large number of participants is typically required couple with extremely fast execution. Another common challenge is the need for some sort of search process. “For example, search may be conducted by members of the mobilized community for survivors after a natural disaster.” Recruiting large numbers of participants, however, requires that individuals be motivated to actually conduct the search and participate in the information diffusion. Clearly, “providing appropriate incentives is a key challenge in social mobilization.”

This explains the rationale behind DARPA decision to launch their Red Balloon Challenge: “to explore the roles the Internet and social networking play in the timely communication, wide-area team-building, and urgent mobilization required to solve broad-scope, time-critical problems.” So 10 red weather balloons were discretely placed at different locations in the continental US. A senior analyst at the National Geospatial-Intelligence Agency is said to have characterized the challenge is impossible for conventional intelligence-gathering methods. Riley’s team found all 10 balloons in 8 hours and 36 minutes. How did they do it?

Some 36 hours before the start of the challenge, the team at MIT had already recruited over 4,000 participants using a “recursive incentive mechanism.” They used the $40,000 prize money that would be awarded by the winners of the challenge as a “financial incentive structure rewarding not only the people who correctly located the balloons but also those connecting the finder [back to the MIT team].” If Riley and colleagues won:

we would allocate $4000 in prize money to each of the 10 balloons. We promised $2000 per balloon to the first person to send in the cor- rect balloon coordinates. We promised $1000 to the person who invited that balloon finder onto the team, $500 to whoever invited the in- viter, $250 to whoever invited that person, and so on. The underlying structure of the “recursive incentive” was that whenever a person received prize money for any reason, the person who in- vited them would also receive money equal to half that awarded to their invitee

In other words, the reward offers by Team MIT “scales with the size of the entire recruitment tree (because larger trees are more likely to succeed), rather than depending solely on the immediate recruited friends.” What is stunning about Riley et al.’s approach is that their “attrition rate” was almost half the rate of other comparable social network experiments. In other words, participants in the MIT recruitment tree were about twice as likely to “play the game” so-to-speak rather than give up. In addition, the number recruited by each individual followed a power law distribution, which suggests a possible tipping point dynamic.

In conclusion, the mechanism devised by the winning team “simultaneously provides incentives for participation and for recruiting more individuals to the cause.” So what insights does this study provide vis-a-vis live crisis mapping initiatives that are volunteer-based, like those spearheaded by the Standby Volunteer Task Force (SBTF) and the Humanitarian OpenStreetMap (HOT) communities? While these networks don’t have any funding to pay volunteers (this would go against the spirit of volunteerism in any case), I think a number of insights can nevertheless be drawn.

In the volunteer sector, the “currency of exchange” is credit. That is, the knowledge and acknowledgement that I participated in the Libya Crisis Map to support the UN’s humanitarian operations, for example. I recently introduced SBTF “deployment badges” to serve in part the public acknowledgment incentive. SBTF volunteers can now add badges for deployments there were engaged in, e.g., “Sudan 2011”; “New Zealand 2011”, etc.

What about using a recursive credit mechanism? For example, it would be ideal if volunteers could find out how a given report they worked on was ultimately used by a humanitarian colleague monitoring a live map. Using the Red Balloon analogy, the person who finds the balloon should be able to reward all those in her “recruitment tree” or in our case “SBTF network”. Lets say Helena works for the UN and used the Libya Crisis Map whilst in Tripoli. She finds an important report on the map and shares this with her colleagues on the Tunisian border who decide to take some kind of action as a result. Now lets say this report came from a tweet that Chrissy in the Media Monitoring Team found while volunteering on the deployment. She shared the tweet with Jess in the GPS Team who found the coordinates for the location referred to in that tweet. Melissa then added this to the live map being monitored by the UN. Wouldn’t be be ideal if each could be sent an email letting them know about Helena’s response? I realize this isn’t trivial to implement but what would have to be in place to make something like this actually happen? Any thoughts?

On the recruitment side, we haven’t really done anything explicitly to incentivize current volunteers to recruit additional volunteers. Could we incentivize this beyond giving credit? Perhaps we could design a game-like point system? Or a fun ranking system with different titles assigned according to the number of volunteers recruited? Another thought would be to simply ask existing volunteers to recruit one or two additional volunteers every year. We currently have about 700 volunteers in the SBTF, so this might be one way to increase substantially in size.

I’m not sure what type of mechanism we could devise to simultaneously provide incentives for participation and recruitment. Perhaps those incentives already exist, in the sense that the SBTF response to international crises, which perhaps serves as a sufficient draw. I’d love to hear what iRevolution readers think, especially if you have good ideas that we could realistically implement!

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Posted in Crisis Mapping, Crowdsourcing, Digital Activism, Social Media

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How to Crowdsource Crisis Response

Posted on September 14, 2011 | 13 comments

I recently had the distinct pleasure of giving this year’s keynote address at the Global Communications Forum (#RCcom on Twitter) organized by the Interna-tional Committee of the Red Cross (ICRC) in Geneva. The conversations that followed were thoroughly fruitful and enjoyable.

Like many other humanitarian organizations, the ICRC is thinking hard about how to manage the social media challenge. In 2010, this study carried out by the American Red Cross (ARC) found that the public increasingly expects humanitarian organizations to respond to pleas for help posted on social media platforms like Facebook, Twitter, etc. The question is, how in the world are humanitarian organizations supposed to handle this significant increase in “customer service” requests? Even during non-emergencies, ARC’s Facebook page receives a large number of comments on a daily basis many of which solicit replies. This figure escalates significantly during crises. So what to do?

The answer, in my opinion, requires some organizational change. Clearly, the dramatic rise in customer service requests posted on social media platforms cannot be managed through existing organizational structures and work flows. Moreover, the vast majority of posted requests don’t reflect life threatening situations. In other words, responses to many requests don’t require professional emergency responders. So humanitarian organizations should consider taking a two-pronged strategy to address the social media challenge. The first is to upgrade their “customer service systems” and the second is to connect these systems with local networks of citizen crisis responders.

How do large private sector companies deal with the social media challenge? Well, some obviously do better than others. (Incidentally, this question was a recurring topic of conversation at the Same Wavelength conference in London where I spoke after Geneva). This explains why I recommended that my ICRC colleagues consider various social media customer service models used in the private sector and identify examples of positive deviance.

The latest innovation in the customer service space was just launched at TechCrunch Disrupt this week. TalkTo “allows consumers to send text messages to any business and get quick responses to questions, feedback, and more.” As TechCrunch writes, “no one wants to wait on the phone, and email can be slow as well. SMS Messaging is a natural form of communication these days and the most efficient for simple questions. It makes sense to bring this communication to businesses.” If successful, I wonder whether TalkTo will add Twitter and Facebook to their service as other communication media.

Some companies leverage crowdsourcing, like Best Buy’s TwelpForce. Over time, Best Buy “found that with some good foundational guideposts and training tools, the crowd began to self-organize and govern itself.  Leaders in the space popped up as coaches, or mentors – and pretty soon they had a really good support network in place.”

On the humanitarian side, the American Red Cross has begun to leverage their trained volunteers to manage responses to the organization’s official Facebook page, for example. With some good foundational guideposts and training tools, they should be able to scale this solution. In some ways, one could say that humanitarian organizations are increasingly required to play the role of “telephone” operator. So I’d be very interested in getting feedback from iRevolution readers on alternative, social media approaches to customer service in the private sector. If you know of any innovative ones, please feel free to share in the comments section below.

The second strategy that humanitarian organizations need to consider is linking this new customer service system to networks of citizen crisis responders. An “operator” on the ARC Facebook page, for example, would triage the incoming posts by “pushing” them into different bins according to topic and urgency. Posts that don’t reflect a life-threatening situation but still require operational response could simply be forwarded to local citizen crisis responders. The rest can be re-routed to professional emergency responders. Geo-fenced alerts from crisis mapping platforms could also play an important role in this respect.

One should remember that the majority of crisis responses are “crowdsourced” by definition since the real first responders are always local communities. For example, “it is well known that in case of earthquakes, such as the one that happened in Mexico City, the assistance to the victims comes first of all from the other survivors […]” (Gilbert 1998). In fact, estimates suggest that, “no more than 10 per cent of survival in emergencies can be contributed to external sources of relief aid” (Hillhorst 2004). So why not connect humanitarian customer service systems to local citizen crisis responders and thereby make the latter’s response more targeted and efficient rather than simply ad hoc? I’ve used the term “crowdfeeding” to describe this idea in previous blog posts like this one and this one. We basically need a Match.com for citizen based crisis response in which both problems and solutions are crowdsourced.

So where are these “new” citizen crisis responders to come from? How about leveraging existing networks like Community Emergency Response Teams (CERTs), the UN Volunteer system (UNVs), Red Cross volunteer networks and platforms like Red Cross Volunteer Match? Why not make use of existing training materials like FEMA’s online courses? Universities could also promote the idea of student crisis responders and offer credit for relevant courses.

Update: New app helps Queensland coordinate volunteers.

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Posted in Crisis Mapping, Crowdsourcing, Humanitarian Technologies

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