Tag Archives: crisis

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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Humanitarian Crisis Computing 101

Disaster-affected communities are increasingly becoming “digital” communities. That is, they increasingly use mobile technology & social media to communicate during crises. I often refer to this user-generated content as Big (Crisis) Data. Humanitarian crisis computing seeks to rapidly identify informative, actionable and credible content in this growing stack of real-time information. The challenge is akin to finding the proverbial needle in the haystack since the vast majority of reports posted on social media is often not relevant for humanitarian response. This is largely a result of the demand versus supply problem described here.

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In any event, the few “needles” of information that are relevant, can relay information that is vital and indeed-life saving for relief efforts—both traditional top-down efforts and more bottom-up grassroots efforts. When disaster strikes, we increasingly see social media traffic explode. We know there are important “pins” of relevant information hidden in this growing stack of information but how do we find them in real-time?

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Humanitarian organizations are ill-equipped to managing the deluge of Big Crisis Data. They tend to sift through the stack of information manually, which means they aren’t able to process more than a small volume of information. This is represented by the dotted green line in the picture below. Big Data is often described as filter failure. Our manual filters cannot manage the large volume, velocity and variety of information posted on social media during disasters. So all the information above the dotted line, Big Data, is completely ignored.

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This is where Advanced Computing comes in. Advanced Computing uses Human and Machine Computing to manage Big Data and reduce filter failure, thus allowing humanitarian organizations to process a larger volume, velocity and variety of crisis information in less time. In other words, Advanced Computing helps us push the dotted green line up the information stack.

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In the early days of digital humanitarian response, we used crowdsourcing to search through the haystack of user-generated content posted during disasters. Note that said content can also include text messages (SMS), like in Haiti. Crowd-sourcing crisis information is not as much fun as the picture below would suggest, however. In fact, crowdsourcing crisis information was (and can still be) quite a mess and a big pain in the haystack. Needless to say, crowdsourcing is not the best filter to make sense of Big Crisis Data.

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Recently, digital humanitarians have turned to microtasking crisis information as described here and here. The UK Guardian and Wired have also written about this novel shift from crowdsourcing to microtasking.

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Microtasking basically turns a haystack into little blocks of stacks. Each micro-stack is then processed by one ore more digital humanitarian volunteers. Unlike crowdsourcing, a microtasking approach to filtering crisis information is highly scalable, which is why we recently launched MicroMappers.

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The smaller the micro-stack, the easier the tasks and the faster that they can be carried out by a greater number of volunteers. For example, instead of having 10 people classify 10,000 tweets based on the Cluster System, microtasking makes it very easy for 1,000 people to classify 10 tweets each. The former would take hours while the latter mere minutes. In response to the recent earthquake in Pakistan, some 100 volunteers used MicroMappers to classify 30,000+ tweets in about 30 hours, for example.

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Machine Computing, in contrast, uses natural language processing (NLP) and machine learning (ML) to “quantify” the haystack of user-generated content posted on social media during disasters. This enable us to automatically identify relevant “needles” of information.

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An example of a Machine Learning approach to crisis computing is the Artificial Intelligence for Disaster Response (AIDR) platform. Using AIDR, users can teach the platform to automatically identify relevant information from Twitter during disasters. For example, AIDR can be used to automatically identify individual tweets that relay urgent needs from a haystack of millions of tweets.

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The pictures above are taken from the slide deck I put together for a keynote address I recently gave at the Canadian Ministry of Foreign Affairs.

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Analyzing Crisis Hashtags on Twitter (Updated)

Update: You can now upload your own tweets to the Crisis Hashtags Analysis Dashboard here

Hashtag footprints can be revealing. The map below, for example, displays the top 200 locations in the world with the most Twitter hashtags. The top 5 are Sao Paolo, London, Jakarta, Los Angeles and New York.

Hashtag map

A recent study (PDF) of 2 billion geo-tagged tweets and 27 million unique hashtags found that “hashtags are essentially a local phenomenon with long-tailed life spans.” The analysis also revealed that hashtags triggered by external events like disasters “spread faster than hashtags that originate purely within the Twitter network itself.” Like other metadata, hashtags can be  informative in and of themselves. For example, they can provide early warning signals of social tensions in Egypt, as demonstrated in this study. So might they also reveal interesting patterns during and after major disasters?

Tens of thousands of distinct crisis hashtags were posted to Twitter during Hurricane Sandy. While #Sandy and #hurricane featured most, thousands more were also used. For example: #SandyHelp, #rallyrelief, #NJgas, #NJopen, #NJpower, #staysafe, #sandypets, #restoretheshore, #noschool, #fail, etc. NJpower, for example, “helped keep track of the power situation throughout the state. Users and news outlets used this hashtag to inform residents where power outages were reported and gave areas updates as to when they could expect their power to come back” (1).

Sandy Hashtags

My colleagues and I at QCRI are studying crisis hashtags to better understand the variety of tags used during and in the immediate aftermath of major crises. Popular hashtags used during disasters often overshadow more hyperlocal ones making these less discoverable. Other challenges include the: “proliferation of hashtags that do not cross-pollinate and a lack of usability in the tools necessary for managing massive amounts of streaming information for participants who needed it” (2). To address these challenges and analyze crisis hashtags, we’ve just launched a Crisis Hashtags Analytics Dashboard. As displayed below, our first case study is Hurricane Sandy. We’ve uploaded about half-a-million tweets posted between October 27th to November 7th, 2012 to the dashboard.

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Users can visualize the frequency of tweets (orange line) and hashtags (green line) over time using different time-steps, ranging from 10 minute to 1 day intervals. They can also “zoom in” to capture more minute changes in the number of hashtags per time interval. (The dramatic drop on October 30th is due to a server crash. So if you have access to tweets posted during those hours, I’d be  grateful if you could share them with us).

Hashtag timeline

In the second part of the dashboard (displayed below), users can select any point on the graph to display the top “K” most frequent hashtags. The default value for K is 10 (e.g., top-10 most frequent hashtags) but users can change this by typing in a different number. In addition, the 10 least-frequent hashtags are displayed, as are the 10 “middle-most” hashtags. The top-10 newest hashtags posted during the selected time are also displayed as are the hashtags that have seen the largest increase in frequency. These latter two metrics, “New K” and “Top Increasing K”, may provide early warning signals during disasters. Indeed, the appearance of a new hashtag can reveal a new problem or need while a rapid increase in the frequency of some hashtags can denote the spread of a problem or need.

QCRI Dashboard 2

The third part of the dashboard allows users to visualize and compare the frequency of top hashtags over time. This feature is displayed in the screenshot below. Patterns that arise from diverging or converging hashtags may indicate important developments on the ground.

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We’re only at the early stages of developing our hashtags analytics platform (above), but we hope the tool will provide insights during future disasters. For now, we’re simply experimenting and tinkering. So feel free to get in touch if you would like to collaborate and/or suggest some research questions.

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Acknowledgements: Many thanks to QCRI colleagues Ahmed Meheina and Sofiane Abbar for their work on developing the dashboard.

Crowdsourcing Critical Thinking to Verify Social Media During Crises

My colleagues and I at QCRI and the Masdar Institute will be launching Verily in the near future. The project has already received quite a bit of media coverage—particularly after the Boston marathon bombings. So here’s an update. While major errors were made in the crowdsourced response to the bombings, social media can help to find quickly find individuals and resources during a crisis. Moreover, time-critical crowdsourcing can also be used to verify unconfirmed reports circulating on social media.

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The errors made following the bombings were the result of two main factors:

(1) the crowd is digitally illiterate
(2) the platforms used were not appropriate for the tasks at hand

The first factor has to do with education. Most of us are still in Kindergarden when it comes to the appropriate use social media. We lack the digital or media literacy required for the responsible use of social media during crises. The good news, however, is that the major backlash from the mistakes made in Boston are already serving as an important lesson to many in the crowd who are very likely to think twice about retweeting certain content or making blind allegations on social media in the future. The second factor has to do with design. Tools like Reddit and 4Chan that are useful for posting photos of cute cats are not always the tools best designed for finding critical information during crises. The crowd is willing to help, this much has been proven. The crowd simply needs better tools to focus and rationalize to goodwill of it’s members.

Verily was inspired from the DARPA Red Balloon Challenge which leveraged social media & social networks to find the location of 10 red weather balloons planted across the continental USA (3 million square miles) in under 9 hours. So Verily uses that same time-critical mobilization approach—negative incentive recursive mechanism—to rapidly collect evidence around a particular claim during a disaster, such as “The bridge in downtown LA has been destroyed by the earthquake”. Users of Verily can share this verification challenge directly from the Verily website (e.g., Share via Twitter, FB, and Email), which posts a link back to the Verily claim page.

This time-critical mobilization & crowdsourcing element is the first main component of Verily. Because disasters are far more geographically bounded than the continental US, we believe that relevant evidence can be crowdsourced in a matter of minutes rather than hours. Indeed, while the degree of separation in the analog world is 6, that number falls closer to 4 on social media, and we believe falls even more in bounded geographical areas like urban centers. This means that the 20+ people living opposite that bridge in LA are only 2 or 3 hops from your social network and could be tapped via Verily to take pictures of the bridge from their window, for example.

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The second main component is to crowdsource critical thinking which is key to countering the spread of false rumors during crises. The interface to post evidence on Verily is modeled along the lines of Pinterest, but with each piece of content (text, image, video), users are required to add a sentence or two to explain why they think or know that piece of evidence is authentic or not. Others can comment on said evidence accordingly. This workflow prompts users to think critically rather than blindly share/RT content on Twitter without much thought, context or explanation. Indeed, we hope that with Verily more people will share links back to Verily pages rather than to out of context and unsubstantiated links of images/videos/claims, etc.

In other words, we want to redirect traffic to a repository of information that incentivises critical thinking. This means Verily is also looking to be an educational tool; we’ll have simple mini-guides on information forensics available to users (drawn from the BBC’s UGC, NPR’s Andy Carvin, etc). While we’ll include dig ups/downs on perceived authenticity of evidence posted to Verily, this is not the main focus of Verily. Dig ups/downs are similar to retweets and simply do not capture/explain whether said digger has voted based on her/his expertise or any critical thinking.

If you’re interested in supporting this project and/or sharing feedback, then please feel free to contact me at any time. For more background information on Verily, kindly see this post.

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Social Media for Emergency Management: Question of Supply and Demand

I’m always amazed by folks who dismiss the value of social media for emergency management based on the perception that said content is useless for disaster response. In that case, libraries are also useless (bar the few books you’re looking for, but those rarely represent more than 1% of all the books available in a major library). Does that mean libraries are useless? Of course not. Is social media useless for disaster response? Of course not. Even if only 0.001% of the 20+ million tweets posted during Hurricane Sandy were useful, and only half of these were accurate, this would still mean over 1,000 real-time and informative tweets, or some 15,000 words—i.e., the equivalent of a 25-page, single-space document exclusively composed of fully relevant, actionable & timely disaster information.

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Empirical studies clearly prove that social media reports can be informative for disaster response. Numerous case studies have also described how social media has saved lives during crises. That said, if emergency responders do not actively or explicitly create demand for relevant and high quality social media content during crises, then why should supply follow? If the 911 emergency number (999 in the UK) were never advertised, then would anyone call? If 911 were simply a voicemail inbox with no instructions, would callers know what type of actionable information to relay after the beep?

While the majority of emergency management centers do not create the demand for crowdsourced crisis information, members of the public are increasingly demanding that said responders monitor social media for “emergency posts”. But most responders fear that opening up social media as a crisis communication channel with the public will result in an unmanageable flood of requests, The London Fire Brigade seems to think otherwise, however. So lets carefully unpack the fear of information flooding.

First of all, New York City’s 911 operators receive over 10 million calls every year that are accidental, false or hoaxes. Does this mean we should abolish the 911 system? Of course not. Now, assuming that 10% of these calls takes an operator 10 seconds to manage, this represents close to 3,000 hours or 115 days worth of “wasted work”. But this filtering is absolutely critical and requires human intervention. In contrast, “emergency posts” published on social media can be automatically filtered and triaged thanks to Big Data Analytics and Social Computing, which could save time operators time. The Digital Operations Center at the American Red Cross is currently exploring this automated filtering approach. Moreover, just as it is illegal to report false emergency information to 911, there’s no reason why the same laws could not apply to social media when these communication channels are used for emergency purposes.

Second, if individuals prefer to share disaster related information and/or needs via social media, this means they are less likely to call in as well. In other words, double reporting is unlikely to occur and could also be discouraged and/or penalized. In other words, the volume of emergency reports from “the crowd” need not increase substantially after all. Those who use the phone to report an emergency today may in the future opt for social media instead. The only significant change here is the ease of reporting for the person in need. Again, the question is one of supply and demand. Even if relevant emergency posts were to increase without a comparable fall in calls, this would simply reveal that the current voice-based system creates a barrier to reporting that discriminates against certain users in need.

Third, not all emergency calls/posts require immediate response by a paid professional with 10+ years of experience. In other words, the various types of needs can be triaged and responded to accordingly. As part of their police training or internships, new cadets could be tasked to respond to less serious needs, leaving the more seasoned professionals to focus on the more difficult situations. While this approach certainly has some limitations in the context of 911, these same limitations are far less pronounced for disaster response efforts in which most needs are met locally by the affected communities themselves anyway. In fact, the Filipino government actively promotes the use of social media reporting and crisis hashtags to crowdsource disaster response.

In sum, if disaster responders and emergency management processionals are not content with the quality of crisis reporting found on social media, then they should do something about it by implementing the appropriate policies to create the demand for higher quality and more structured reporting. The first emergency telephone service was launched in London some 80 years ago in response to a devastating fire. At the time, the idea of using a phone to report emergencies was controversial. Today, the London Fire Brigade is paving the way forward by introducing Twitter as a reporting channel. This move may seem controversial to some today, but give it a few years and people will look back and ask what took us so long to adopt new social media channels for crisis reporting.

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Tweets, Crises and Behavioral Psychology: On Credibility and Information Sharing

How we feel about the content we read on Twitter influences whether we accept and share it—particularly during disasters. My colleague Yasuaki Sakamoto at the Stevens Institute of Technology (SIT) and his PhD students analyzed this dyna-mic more closely in this recent study entitled “Perspective Matters: Sharing of Crisis Information in Social Media”. Using a series behavioral psychology experiments, they examined “how individuals share information related to the 9.0 magnitude earthquake, which hit northeastern Japan on March 11th, 2011.” Their results indicate that individuals were more likely to share crisis infor-mation (1) when they imagined that they were close to the disaster center, (2) when they were thinking about themselves, and (3) when they experienced negative emotions as a result of reading the information.

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Yasu and team are particularly interested in “the effects of perspective taking – considering self or other – and location on individuals’ intention to pass on information in a Twitter-like environment.” In other words: does empathy influence information sharing (retweeting) during crises? Does thinking of others in need eliminate the individual differences in perception that arise when thinking of one’s self instead? The authors hypothesize that “individuals’ information sharing decision can be influenced by (1) their imagined proximity, being close to or distant from the disaster center, (2) the perspective that they take, thinking about self or other, and (3) how they feel about the information that they are exposed to in social media, positive, negative or neutral.”

To test these hypotheses, Yasu and company collected one year’s worth of tweets posted by two major news agencies and five individuals following the Japan Earthquake on March 11, 2012. They randomly sampled 100 media tweets and 100 tweets produced by individuals, resulting a combined sample of 200 tweets. Sampling from these two sources (media vs user-generated) enables Yasu and team to test whether people treat the resulting content differently. Next, they recruited 468 volunteers from Amazon’s Mechanical Turk and paid them a nominal fee for their participation in a series of three behavioral psychology experiments.

In the first experiment, the “control” condition, volunteers read through the list of tweets and simply rated the likelihood of sharing a given tweet. The second experiment asked volunteers to read through the list and imagine they were in Fukushima. They were then asked to document their feelings and rate whether they would pass along a given message. Experiment three introduced a hypo-thetical person John based in Fukushima and prompted users to describe how each tweet might make John feel and rate whether they would share the tweet.

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The results of these experiments suggest that, “people are more likely to spread crisis information when they think about themselves in the disaster situation. During disasters, then, one recommendation we can give to citizens would be to think about others instead of self, and think about others who are not in the disaster center. Doing so might allow citizens to perceive the information in a different way, and reduce the likelihood of impulsively spreading any seemingly useful but false information.” Yasu and his students also found that “people are more likely to share information associated with negative feelings.” Since rumors tend to evoke negativity,” they spread more quickly. The authors entertain possible ways to manage this problem such as “surrounding negative messages with positive ones,” for example.

In conclusion, Yasu and his students consider the design principles that ought to be considered when designing social media systems to verify and counter rumors. “In practice, designers need to devote significant efforts to understanding the effects of perspective taking and location, as shown in the current work, and develop techniques to mitigate negative influences of unproved information in social media.”

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For more on Yasu’s work, see:

  • Using Crowdsourcing to Counter False Rumos on Social Media During Crises [Link]

Using #Mythbuster Tweets to Tackle Rumors During Disasters

The massive floods that swept through Queensland, Australia in 2010/2011 put an area almost twice the size of the United Kingdom under water. And now, a year later, Queensland braces itself for even worse flooding:

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More than 35,000 tweets with the hashtag #qldfloods were posted during the height of the flooding (January 10-16, 2011). One of the most active Twitter accounts belonged to the Queensland Police Service Media Unit: @QPSMedia. Tweets from (and to) the Unit were “overwhelmingly focussed on providing situational information and advice” (1). Moreover, tweets between @QPSMedia and followers were “topical and to the point, significantly involving directly affected local residents” (2). @QPSMedia also “introduced innovations such as the #Mythbuster series of tweets, which aimed to intervene in the spread of rumor and disinformation” (3).

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On the evening of January 11, @QPSMedia began to post a series of tweets with #Mythbuster in direct response to rumors and misinformation circulating on Twitter. Along with official notices to evacuate, these #Mythbuster tweets were the most widely retweeted @QPSMedia messages.” They were especially successful. Here is a sample: “#mythbuster: Wivenhoe Dam is NOT about to collapse! #qldfloods”; “#mythbuster: There is currently NO fuel shortage in Brisbane. #qldfloods.”

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This kind of pro-active intervention reminds me of the #fakesandy hashtag used during Hurricane Sandy and FEMA’s rumor control initiative during Hurricane Sandy. I expect to see greater use of this approach by professional emergency responders in future disasters. There’s no doubt that @QPSMedia will provide this service again with the coming floods and it appears that @QLDonline is already doing so (above tweet). Brisbane’s City Council has also launched this Crowdmap marking latest road closures, flood areas and sandbag locations. Hoping everyone in Queensland stays safe!

In the meantime, here are some relevant statistics on the crisis tweets posted during the 2010/2011 floods in Queensland:

  • 50-60% of #qldfloods messages were retweets (passing along existing messages, and thereby  making them more visible); 30-40% of messages contained links to further information elsewhere on the Web.
  • During the crisis, a number of Twitter users dedicated themselves almost exclusively to retweeting #qldfloods messages, acting as amplifiers of emergency information and thereby increasing its reach.
  • #qldfloods tweets largely managed to stay on topic and focussed predominantly on sharing directly relevant situational information, advice, news media and multimedia reports.
  • Emergency services and media organisations were amongst the most visible participants in #qldfloods, especially also because of the widespread retweeting of their messages.
  • More than one in every five shared links in the #qldfloods dataset was to an image hosted on one of several image-sharing services; and users overwhelmingly depended on Twitpic and other Twitter-centric image-sharing services to upload and distribute the photographs taken on their smartphones and digital cameras
  • The tenor of tweets during the latter days of the immediate crisis shifted more strongly towards organising volunteering and fundraising efforts: tweets containing situational information and advice, and news media and multimedia links were retweeted disproportionately often.
  • Less topical tweets were far less likely to be retweeted.