Category Archives: Big Data

Calling All Digital Jedis: Support UN Response to Super Typhoon Ruby!

The United Nations has officially activated the Digital Humanitarian Network (DHN) in response to Typhoon Ruby. The DHN serves as the official interface between formal humanitarian organizations and digital volunteer groups from all around the world. These digital volunteers—also known as Digital Jedis— provide humanitarian organizations like the UN and the Red Cross with the “surge” capacity they need to make sense of the “Big Data” that gets generated during disasters. This “Big Data” includes large volumes of social media reports and satellite imagery, for example. And there is a lot of this data being generated right now as a result of Super Typhoon Ruby.

Typhoon Ruby

To make sense of this flash flood of information, Digital Jedis use crowdsourcing platforms like MicroMappers, which was developed in partnership with the UN Office for the Coordination of Humanitarian Affairs (OCHA). In their activation of the Digital Humanitarian Network, the UN has requested that Digital Jedis look for Ruby-related tweets that refer to needs, damage & response efforts. They have also asked digital volunteers to identify pictures of damage caused by the Typhoon. These tweets and pictures will then to be added to a live crisis map to augment the UN’s own disaster damage and needs assessment efforts.

You too can be a Digital Jedi. Trust me, MicroMappers is far easier to use than a lightsaber. All it takes is a single Click of the mouse. Yes, it really is that simple. So, if a Digital Jedi you want to be, let your first Click be this one! Following that click will set you on the path to help the United Nation’s important relief efforts in the Philippines. So if you’ve got a bit of time on your hands—even 2 minutes goes a long way—then help us make a meaningful difference in the world, join the Force! And may the Crowd be with Us!

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See also: Digital Humanitarians – The Path of the Digtal Jedis

Digital Jedis: There Has Been An Awakening…

Crowdsourcing and Humanitarian Action: Analysis of the Literature

Raphael Hörler from Zurich’s ETH University has just completed his thesis on the role of crowdsourcing in humanitarian action. His valuable research offers one of the most up-to-date and comprehensive reviews of the principal players and humanitarian technologies in action today. In short, I highly recommend this important resource. Raphael’s full thesis is available here (PDF).

Crowdsourcing Yolanda Response

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Computing Research Institutes as an Innovation Pathway for Humanitarian Technology

The World Humanitarian Summit (WHS) is an initiative by United Nations Secretary-General Ban Ki-moon to improve humanitarian action. The Summit, which is to be held in 2016, stands to be one of the most important humanitarian conferences in a decade. One key pillar of WHS is humanitarian innovation. “Transformation through Innovation” is the WHS Working Group dedicated to transforming humanitarian action by focusing explicitly on innovation. I have the pleasure of being a member of this working group where my contribution focuses on the role of new technologies, data science and advanced computing. As such, I’m working on an applied study to explore the role of computing research institutes as an innovation pathway for humanitarian technology. The purpose of this blog post is to invite feedback on the ideas presented below.

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I first realized that the humanitarian community faced a “Big Data” challenge in 2010, just months after I had joined Ushahidi as Director of Crisis Mapping, and just months after co-founding CrisisMappers: The Humanitarian Technology Network. The devastating Haiti Earthquake resulted in a massive overflow of information generated via mainstream news, social media, text messages and satellite imagery. I launched and spearheaded the Haiti Crisis Map at the time and together with hundreds of digital volunteers from all around the world went head-to head with Big Data. As noted in my forthcoming book, we realized there and then that crowdsourcing and mapping software alone were no match for Big (Crisis) Data.

Digital Humanitarians: The Book

This explains why I decided to join an advanced computing research institute, namely QCRI. It was clear to me after Haiti that humanitarian organizations had to partner directly with advanced computing experts to manage the new Big Data challenge in disaster response. So I “embedded” myself in an institute with leading experts in Big Data Analytics, Data Science and Social Computing. I believe that computing research institutes (CRI’s) can & must play an important role in fostering innovation in next generation humanitarian technology by partnering with humanitarian organizations on research & development (R&D).

There is already some evidence to support this proposition. We (QCRI) teamed up with the UN Office for the Coordination of Humanitarian Affairs (OCHA) to create the Artificial Intelligence for Disaster Response platform, AIDR as well as MicroMappers. We are now extending AIDR to analyze text messages (SMS) in partnership with UNICEF. We are also spearheading efforts around the use and analysis of aerial imagery (captured via UAVs) for disaster response (see the Humanitarian UAV Network: UAViators). On the subject of UAVs, I believe that this new technology presents us (in the WHS Innovation team) with an ideal opportunity to analyze in “real time” how a new, disruptive technology gets adopted within the humanitarian system. In addition to UAVs, we catalyzed a partnership with Planet Labs and teamed up with Zooniverse to take satellite imagery analysis to the next level with large scale crowd computing. To this end, we are working with humanitarian organizations to enable them to make sense of Big Data generated via social media, SMS, aerial imagery & satellite imagery.

The incentives for humanitarian organizations to collaborate with CRI’s are obvious, especially if the latter (like QCRI) commits to making the resulting prototypes freely accessible and open source. But why should CRI’s collaborate with humanitarian organizations in the first place? Because the latter come with real-world challenges and unique research questions that many computer scientists are very interested in for several reasons. First, carrying out scientific research on real-world problems is of interest to the vast majority of computer scientists I collaborate with, both within QCRI and beyond. These scientists want to apply their skills to make the world a better place. Second, the research questions that humanitarian organizations bring enable computer scientists to differentiate themselves in the publishing world. Third, the resulting research can help advanced the field of computer science and advanced computing.

So why are we see not seeing more collaboration between CRI’s & humanitarian organizations? Because of this cognitive surplus mismatch. It takes a Director of Social Innovation (or related full-time position) to serve as a translational leader between CRI’s and humanitarian organizations. It takes someone (ideally a team) to match the problem owners and problem solvers; to facilitate and manage the collaboration between these two very different types of expertise and organizations. In sum, CRI’s can serve as an innovation pathway if the following three ingredients are in place: 1) Translation Leader; 2) Committed CRI; and 3) Committed Humanitarian Organization. These are necessary but not sufficient conditions for success.

While research institutes have a comparative advantage in R&D, they are not the best place to scale humanitarian technology prototypes. In order to take these prototypes to the next level, make them sustainable and have them develop into enterprise level software, they need to be taken up by for-profit companies. The majority of CRI’s (QCRI included) actually do have a mandate to incubate start-up companies. As such, we plan to spin-off some of the above platforms as independent companies in order to scale the technologies in a robust manner. Note that the software will remain free to use for humanitarian applications; other uses of the platform will require a paid license. Therein lies the end-to-end innovation path that computing research institutes can offer humanitarian organization vis-a-vis next generation humanitarian technologies.

As noted above, part of my involvement with the WHS Innovation Team entails working on an applied study to document and replicate this innovation pathway. As such, I am looking for feedback on the above as well as on the research methodology described below.

I plan to interview Microsoft Research, IBM Research, Yahoo Research, QCRI and other institutes as part of this research. More specifically, the interview questions will include:

  • Have you already partnered with humanitarian organizations? Why/why not?
  • If you have partnered with humanitarian organizations, what was the outcome? What were the biggest challenges? Was the partnership successful? If so, why? If not, why not?
  • If you have not yet partnered with humanitarian organizations, why not? What factors would be conducive to such partnerships and what factors serve as hurdles?
  • What are your biggest concerns vis-a-vis working with humanitarian groups?
  • What funding models did you explore if any?

I also plan to interview humanitarian organizations to better understand the prospects for this potential innovation pathway. More specifically, I plan to interview ICRC, UNHCR, UNICEF and OCHA using the following questions:

  • Have you already partnered with computing research groups? Why/why not?
  • If you have partnered with computing research groups, what was the outcome? What were the biggest challenges? Was the partnership successful? If so, why? If not, why not?
  • If you have not yet partnered with computing research groups, why not? What factors would be conducive to such partnerships and what factors serve as hurdles?
  • What are your biggest concerns vis-a-vis working with computing research groups?
  • What funding models did you explore if any?

My plan is to carry out the above semi-structured interviews in February-March 2015 along with secondary research. My ultimate aim with this deliverable is to develop a model to facilitate greater collaboration between computing research institutes and humanitarian organizations. To this end, I welcome feedback on all of the above (feel free to email me and/or add comments below). Thank you.

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See also:

  • Research Framework for Next Generation Humanitarian Technology and Innovation [link]
  • From Gunfire at Sea to Maps of War: Implications for Humanitarian Innovation [link]

Establishing Social Media Hashtag Standards for Disaster Response

The UN Office for the Coordination of Humanitarian Affairs (OCHA) has just published an important, must-read report on the use of social media for disaster response. As noted by OCHA, this document was inspired by conversations with my team and I at QCRI. We jointly recognize that innovation in humanitarian technology is not enough. What is needed—and often lacking—is innovation in policymaking. Only then can humanitarian technology have widespread impact. This new think piece by OCHA seeks to catalyze enlightened policymaking.

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I was pleased to provide feedback on earlier drafts of this new study and look forward to discussing the report’s recommendations with policymakers across the humanitarian space. In the meantime, many thanks to Roxanne Moore and Andrej Verity for making this report a reality. As Andrej notes in his blog post on this new study, the Filipino Government has just announced that “twitter will become another source of information for the Philippines official emergency response mechanism,” which will lead to an even more pressing Big (Crisis) Data challenge. The use of standardized hashtags will thus be essential.

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The overflow of information generated during disasters can be as paralyzing to disaster response as the absence of information. While information scarcity has long characterized our information landscapes, today’s information-scapes are increasingly marked by an overflow of information—Big Data. To this end, encouraging the proactive standardization of hashtags may be one way to reduce this Big Data challenge. Indeed, standardized hashtags—i.e., more structured information—would enable paid emergency responders (as well as affected communities) to “better leverage crowdsourced information for operational planning and response.” At present, the Government of the Philippines seems to be the few actors that actually endorse the use of specific hashtags during major disasters as evidenced by their official crisis hashtags strategy.

The OCHA report thus proposes three hashtag standards and also encourages social media users to geo-tag their content during disasters. The latter can be done by enabling auto-GPS tagging or by using What3Words. Users should of course be informed of data-privacy considerations when geo-tagging their reports. As for the three hashtag standards:

  1. Early standardization of hashtags designating a specific disaster
  2. Standard, non-changing hashtag for reporting non-emergency needs
  3. Standard, non-changing hashtags for reporting emergency needs

1. As the OCHA think piece rightly notes, “News stations have been remarkably successful in encouraging early standardization of hashtags, especially during political events.” OCHA thus proposes that humanitarian organizations take a “similar approach for emergency response reporting and develop partnerships with Twitter as well as weather and news teams to publicly encourage such standardization. Storm cycles that create hurricanes and cyclones are named prior to the storm. For these events, an official hashtag should be released at the same time as the storm announcement.” For other hazards, “emergency response agencies should monitor the popular hashtag identifying a disaster, while trying to encourage a standard name.”

2. OCHA advocates for the use of #iSee, #iReport or #PublicRep for members of the public to designate tweets that refer to non-emergency needs such as “power lines, road closures, destroyed bridges, large-scale housing damage, population displacement or geographic spread (e.g., fire or flood).” When these hashtags are accompanied with GPS information, “responders can more easily identify and verify the information, therefore supporting more timely response & facilitating recovery.” In addition, responders can more easily create live crisis maps on the fly thanks to this structured, geo-tagged information.

3. As for standard hashtags for emergency reports, OCHA notes emergency calls are starting to give way to emergency SMS’s. Indeed, “Cell phone users will soon be able to send an SMS to a toll-free phone number. For emergency reporting, this new technology could dramatically alter the way the public interacts with nation-based emergency response call centers. It does not take a large imaginary leap to see the potential move from SMS emergency calls to social media emergency calls. Hashtags could be one way to begin reporting emergencies through social media.”

Most if not all countries have national emergency phone numbers already. So OCHA suggests using these existing, well-known numbers as the basis for social media hashtags. More specifically, an emergency hashtag would be composed of the country’s emergency number (such as 911 in the US, 999 in the UK, 133 in Austria, etc) followed by the country’s two-letter code (US, UK, AT respectively). In other words: #911US, #999UK, #133AT. Some countries, like Austria, have different emergency phone numbers for different types of emergencies. So these could also be used accordingly. OCHA recognizes that many “federal agencies fear that such a system would result in people reporting through social media outside of designated monitoring times. This is a valid concern. However, as with the implementation of any new technology in the public service, it will take time and extensive promotion to ensure effective use.”

Digital Humanitarians: The Book

Of course, “no monitoring system will be perfect in terms of low-cost, real-time analysis and high accuracy.” OCHA knows very well that there are a number of important limitations to the system they propose above. To be sure, “significant steps need to be taken to ensure that information flows from the public to response agencies and back to the public through improved efforts.” This is an important theme in my forthcoming book “Digital Humanitarians.”

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See also:

  • Social Media & Emergency Management: Supply and Demand [link]
  • Using AIDR to Automatically Classify Disaster Tweets [link]

Using Flash Crowds to Automatically Detect Earthquakes & Impact Before Anyone Else

It is said that our planet has a new nervous system; a digital nervous system comprised of digital veins and intertwined sensors that capture the pulse of our planet in near real-time. Next generation humanitarian technologies seek to leverage this new nervous system to detect and diagnose the impact of disasters within minutes rather than hours. To this end, LastQuake may be one of the most impressive humanitarian technologies that I have recently come across. Spearheaded by the European-Mediterranean Seismological Center (EMSC), the technology combines “Flashsourcing” with social media monitoring to auto-detect earthquakes before they’re picked up by seismometers or anyone else.

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Scientists typically draw on ground-motion prediction algorithms and data on building infrastructure to rapidly assess an earthquake’s potential impact. Alas, ground-motion predictions vary significantly and infrastructure data are rarely available at sufficient resolutions to accurately assess the impact of earthquakes. Moreover, a minimum of three seismometers are needed to calibrate a quake and said seismic data take several minutes to generate. This explains why the EMSC uses human sensors to rapidly collect relevant data on earthquakes as these reduce the uncertainties that come with traditional rapid impact assess-ment methodologies. Indeed, the Center’s important work clearly demonstrates how the Internet coupled with social media are “creating new potential for rapid and massive public involvement by both active and passive means” vis-a-vis earthquake detection and impact assessments. Indeed, the EMSC can automatically detect new quakes within 80-90 seconds of their occurrence while simultaneously publishing tweets with preliminary information on said quakes, like this one:

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In reality, the first human sensors (increases in web traffic) can be detected within 15 seconds (!) of a quake. The EMSC’s system continues to auto-matically tweet relevant information (including documents, photos, videos, etc.), for the first 90 minutes after it first detects an earthquake and is also able to automatically create a customized and relevant hashtag for individual quakes.

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How do they do this? Well, the team draw on two real-time crowdsourcing methods that “indirectly collect information from eyewitnesses on earthquakes’ effects.” The first is TED, which stands for Twitter Earthquake Detection–a system developed by the US Geological Survey (USGS). TED filters tweets by key word, location and time to “rapidly detect sharing events through increases in the number of tweets” related to an earthquake. The second method, called “flashsourcing” was developed by the European-Mediterranean to analyze traffic patterns on its own website, “a popular rapid earthquake information website.” The site gets an average of 1.5 to 2 million visits a month. Flashsourcing allows the Center to detect surges in web traffic that often occur after earthquakes—a detection method named Internet Earthquake Detection (IED). These traffic surges (“flash crowds”) are caused by “eyewitnesses converging on its website to find out the cause of their shaking experience” and can be detected by analyzing the IP locations of website visitors.

It is worth emphasizing that both TED and IED work independently from traditional seismic monitoring systems. Instead, they are “based on real-time statistical analysis of Internet-based information generated by the reaction of the public to the shaking.” As EMSC rightly notes in a forthcoming peer-reviewed scientific study, “Detections of felt earthquakes are typically within 2 minutes for both methods, i.e., considerably faster than seismographic detections in poorly instrumented regions of the world.” TED and IED are highly complementary methods since they are based on two entirely “different types of Internet use that might occur after an earthquake.” TED depends on the popularity of Twitter while IED’s effectiveness depends on how well known the EMSC website is in the area affected by an earthquake. LastQuake automatically publishes real-time information on earthquakes by automatically merging real-time data feeds from both TED and IED as well as non-crowdsourcing feeds.

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Lets looks into the methodology that powers IED. Flashsourcing can be used to detect felt earthquakes and provide “rapid information (within 5 minutes) on the local effects of earthquakes. More precisely, it can automatically map the area where shaking was felt by plotting the geographical locations of statistically significant increases in traffic […].” In addition, flashsourcing can also “discriminate localities affected by alarming shaking levels […], and in some cases it can detect and map areas affected by severe damage or network disruption through the concomitant loss of Internet sessions originating from the impacted region.” As such, this “negative space” (where there are no signals) is itself an important signal for damage assessment, as I’ve argued before.

remypicIn the future, EMSC’s flashsourcing system may also be able discriminate power cuts between indoor and outdoor Internet connections at the city level since the system’s analysis of web traffic session will soon be based on web sockets rather than webserver log files. This automatic detection of power failures “is the first step towards a new system capable of detecting Internet interruptions or localized infrastructure damage.” Of course, flashsourcing alone does not “provide a full description of earthquake impact, but within a few minutes, independently of any seismic data, and, at little cost, it can exclude a number of possible damage scenarios, identify localities where no significant damage has occurred and others where damage cannot be excluded.”

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EMSC is complementing their flashsourching methodology with a novel mobile app that quickly enables smartphone users to report about felt earthquakes. Instead of requiring any data entry and written surveys, users simply click on cartoonish-type pictures that best describe the level of intensity they felt when the earthquake (or aftershocks) struck. In addition, EMSC analyzes and manually validates geo-located photos and videos of earthquake effects uploaded to their website (not from social media). The Center’s new app will also make it easier for users to post more pictures more quickly.

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What about typical criticisms (by now broken records) that social media is biased and unreliable (and thus useless)? What about the usual theatrics about the digital divide invalidating any kind of crowdsourcing effort given that these will be heavily biased and hardly representative of the overall population? Despite these already well known short-comings and despite the fact that our inchoate digital networks are still evolving into a new nervous system for our planet, the existing nervous system—however imperfect and immature—still adds value. TED and LastQuake demonstrate this empirically beyond any shadow of a doubt. What’s more, the EMSC have found that crowdsourced, user-generated information is highly reliable: “there are very few examples of intentional misuses, errors […].”

My team and I at QCRI are honored to be collaborating with EMSC on integra-ting our AIDR platform to support their good work. AIDR enables uses to automatically detect tweets of interest by using machine learning (artificial intelligence) which is far more effective searching for keywords. I recently spoke with Rémy Bossu, one masterminds behind the EMSC’s LastQuake project about his team’s plans for AIDR:

“For us AIDR could be a way to detect indirect effects of earthquakes, and notably triggered landslides and fires. Landslides can be the main cause of earthquake losses, like during the 2001 Salvador earthquake. But they are very difficult to anticipate, depending among other parameters on the recent rainfalls. One can prepare a susceptibility map but whether there are or nor landslides, where they have struck and their extend is something we cannot detect using geophysical methods. For us AIDR is a tool which could potentially make a difference on this issue of rapid detection of indirect earthquake effects for better situation awareness.”

In other words, as soon as the EMSC system detects an earthquake, the plan is for that detection to automatically launch an AIDR deployment to automatically identify tweets related to landslides. This integration is already completed and being piloted. In sum, EMSC is connecting an impressive ecosystem of smart, digital technologies powered by a variety of methodologies. This explains why their system is one of the most impressive & proven examples of next generation humanitarian technologies that I’ve come across in recent months.

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Acknowledgements: Many thanks to Rémy Bossu for providing me with all the material and graphics I needed to write up this blog post.

See also:

  • Social Media: Pulse of the Planet? [link]
  • Taking Pulse of Boston Bombings [link]
  • The World at Night Through the Eyes of the Crowd [link]
  • The Geography of Twitter: Mapping the Global Heartbeat [link]

The Filipino Government’s Official Strategy on Crisis Hashtags

As noted here, the Filipino Government has had an official strategy on promoting the use of crisis hashtags since 2012. Recently, the Presidential Communications Development and Strategic Planning Office (PCDSPO) and the Office of the Presidential Spokesperson (PCDSPO-OPS) have kindly shared their their 7-page strategy (PDF), which I’ve summarized below.

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The Filipino government first endorsed the use of the #rescuePH and #reliefPH in August 2012, when the country was experiencing storm-enhanced monsoon rains. These were initiatives from the private sector. Enough people were using the hashtags to make them trend for days. Eventually, we adopted the hashtags in our tweets for disseminating government advisories, and for collecting reports from the ground. We also ventured into creating new hashtags, and into convincing media outlets to use unified hashtags.” For new hashtags, “The convention is the local name of the storm + PH (e.g., #PabloPH, #YolandaPH). In the case of the heavy monsoon, the local name of the monsoon was used, plus the year (i.e., #Habagat2013).” After agreeing on the hashtags, ” the OPS issued an official statement to the media and the public to carry these hashtags when tweeting about weather-related reports.”

The Office of the Presidential Spokesperson (OPS) would then monitor the hashtags and “made databases and lists which would be used in aid of deployed government frontline personnel, or published as public information.” For example, the OPS  “created databases from reports from #rescuePH, containing the details of those in need of rescue, which we endorsed to the National Disaster Risk Reduction & Management Council, the Coast Guard, and the Department of Transportation and Communications. Needless to say, we assumed that the databases we created using these hashtags would be contaminated by invalid reports, such as spam & other inappropriate messages. We try to filter out these erroneous or malicious reports, before we make our official endorsements to the concerned agencies. In coordination with officers from the Department of Social Welfare and Development, we also monitored the hashtag #reliefPH in order to identify disaster survivors who need food and non-food supplies.”

During Typhoon Haiyan (Yolanda), “the unified hashtag #RescuePH was used to convey lists of people needing help.” This information was then sent to to the National Disaster Risk Reduction & Management Council so that these names could be “included in their lists of people/communities to attend to.” This rescue hashtag was also “useful in solving surplus and deficits of goods between relief operations centers.” So the government encouraged social media users to coordinate their #ReliefPH efforts with the Department of Social Welfare and Development’s on-the-ground relief-coordination efforts. The Government also “created an infographic explaining how to use the hashtag #RescuePH.”

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Earlier, during the 2012 monsoon rains, the government “retweeted various updates on the rescue and relief operations using the hashtag #SafeNow. The hashtag is used when the user has been rescued or knows someone who has been rescued. This helps those working on rescue to check the list of pending affected persons or families, and update it.”

The government’s strategy document also includes an assessment on their use of unified hashtags during disasters. On the positive side, “These hashtags were successful at the user level in Metro Manila, where Internet use penetration is high. For disasters in the regions, where internet penetration is lower, Twitter was nevertheless useful for inter-sector (media – government – NGOs) coordination and information dissemination.” Another positive was the use of a unified hashtag following the heavy monsoon rains of 2012, “which had damaged national roads, inconvenienced motorists, and posing difficulty for rescue operations. After the floods subsided, the government called on the public to identify and report potholes and cracks on the national highways of Metro Manila by tweeting pictures and details of these to the official Twitter account […] , and by using the hashtag #lubak2normal. The information submitted was entered into a database maintained by the Department of Public Works and Highways for immediate action.”

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The hashtag was used “1,007 times within 2 hours after it was launched. The reports were published and locations mapped out, viewable through a page hosted on the PCDSPO website. Considering the feedback, we considered the hashtag a success. We attribute this to two things: one, we used a platform that was convenient for the public to report directly to the government; and two, the hashtag appealed to humor (lubak means potholes or rubble in the vernacular). Furthermore, due to the novelty of it, the media had no qualms helping us spread the word. All the reports we gathered were immediately endorsed […] for roadwork and repair.” This example points to the potential expanded use of social media and crowdsourcing for rapid damage assessments.

On the negative side, the use of #SafeNow resulted mostly in “tweets promoting #safenow, and very few actually indicating that they have been successfully rescued and/or are safe.” The most pressing challenge, however, was filtering. “In succeeding typhoons/instances of flooding, we began to have a filtering problem, especially when high-profile Twitter users (i.e., pop-culture celebrities) began to promote the hashtags through Twitter. The actual tweets that were calls for rescue were being drowned by retweets from fans, resulting in many nonrescue-related tweets […].” This explains the need for Twitter monitoring platforms like AIDR, which is free and open source.

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