Tag Archives: Pablo

Using CrowdFlower to Microtask Disaster Response

Cross-posted from CrowdFlower blog

A devastating earthquake struck Port-au-Prince on January 12, 2010. Two weeks later, on January 27th, a CrowdFlower was used to translate text messages from Haitian Creole to English. Tens of thousands of messages were sent by affected Haitians over the course of several months. All of these were heroically translated by hundreds of dedicated Creole-speaking volunteers based in dozens of countries across the globe. While Ushahidi took the lead by developing the initial translation platform used just days after the earthquake, the translation efforts were eventually rerouted to CrowdFlower. Why? Three simple reasons:

  1. CrowdFlower is one of the leading and most highly robust micro-tasking platforms there is;
  2. CrowdFlower’s leadership is highly committed to supporting digital humanitarian response efforts;
  3. Haitians in Haiti could now be paid for their translation work.

While the CrowdFlower project was launched 15 days after the earthquake, i.e., following the completion of search and rescue operations, every single digital humanitarian effort in Haiti was reactive. The key takeaway here was the proof of concept–namely that large-scale micro-tasking could play an important role in humanitarian information management. This was confirmed months later when devastating floods inundated much of Pakistan. CrowdFlower was once again used to translate incoming messages from the disaster affected population. While still reactive, this second use of CrowdFlower demonstrated replicability.

The most recent and perhaps most powerful use of CrowdFlower for disaster response occurred right after Typhoon Pablo devastated the Philippines in early December 2012. The UN Office for the Coordination of Humanitarian Affairs (OCHA) activated the Digital Humanitarian Network (DHN) to rapidly deliver a detailed dataset of geo-tagged pictures and video footage (posted on Twitter) depicting the damage caused by the Typhoon. The UN needed this dataset within 12 hours, which required that 20,000 tweets to be analyzed as quickly as possible. The Standby Volunteer Task Force (SBTF), a member of Digital Huma-nitarians, immediately used CrowdFlower to identify all tweets with links to pictures & video footage. SBTF volunteers subsequently analyzed those pictures and videos for damage and geographic information using other means.

This was the most rapid use of CrowdFlower following a disaster. In fact, this use of CrowdFlower was pioneering in many respects. This was the first time that a member of the Digital Humanitarian Network made use of CrowdFlower (and thus micro-tasking) for disaster response. It was also the first time that Crowd-Flower’s existing workforce was used for disaster response. In addition, this was the first time that data processed by CrowdFlower contributed to an official crisis map produced by the UN for disaster response (see above).

These three use-cases, Haiti, Pakistan and the Philippines, clearly demonstrate the added value of micro-tasking (and hence CrowdFlower) for disaster response. If CrowdFlower had not been available in Haiti, the alternative would have been to pay a handful of professional translators. The total price could have come to some $10,000 for 50,000 text messages (at 0.20 cents per word). Thanks to CrowdFlower, Haitians in Haiti were given the chance to make some of that money by translating the text messages themselves. Income generation programs are absolutely critical to rapid recovery following major disasters. In Pakistan, the use of CrowdFlower enabled Pakistani students and the Diaspora to volunteer their time and thus accelerate the translation work for free. Following Typhoon Pablo, paid CrowdFlower workers from the Philippines, India and Australia categorized several thousand tweets in just a couple hours while the volunteers from the Standby Volunteer Task Force geo-tagged the results. Had CrowdFlower not been available then, it is highly, highly unlikely that the mission would have succeeded given the very short turn-around required by the UN.

While impressive, the above use-cases were also reactive. We need to be a lot more pro-active, which is why I’m excited to be collaborating with CrowdFlower colleagues to customize a standby platform for use by the Digital Humanitarian Network. Having a platform ready-to-go within minutes is key. And while digital volunteers will be able to use this standby platform, I strongly believe that paid CrowdFlower workers also have a key role to play in the digital huma-nitarian ecosystem. Indeed, CrowdFlower’s large, multinational and multi-lingual global workforce is simply unparalleled and has the distinct advantage of being very well versed in the CrowdFlower platform.

In sum, it is high time that the digital humanitarian space move from crowd-sourcing to micro-tasking. It has been three years since the tragic earthquake in Haiti but we have yet to adopt micro-tasking more widely. CrowdFlower should thus play a key role in promoting and enabling this important shift. Their con-tinued important leadership in digital humanitarian response should also serve as a model for other private sector companies in the US and across the globe.


How the UN Used Social Media in Response to Typhoon Pablo (Updated)

Our mission as digital humanitarians was to deliver a detailed dataset of pictures and videos (posted on Twitter) which depict damage and flooding following the Typhoon. An overview of this digital response is available here. The task of our United Nations colleagues at the Office of the Coordination of Humanitarian Affairs (OCHA), was to rapidly consolidate and analyze our data to compile a customized Situation Report for OCHA’s team in the Philippines. The maps, charts and figures below are taken from this official report (click to enlarge).

Typhon PABLO_Social_Media_Mapping-OCHA_A4_Portrait_6Dec2012

This map is the first ever official UN crisis map entirely based on data collected from social media. Note the “Map data sources” at the bottom left of the map: “The Digital Humanitarian Network’s Solution Team: Standby Volunteer Task Force (SBTF) and Humanity Road (HR).” In addition to several UN agencies, the government of the Philippines has also made use of this information.

Screen Shot 2012-12-08 at 7.26.19 AM

Screen Shot 2012-12-08 at 7.29.24 AM

The cleaned data was subsequently added to this Google Map and also made public on the official Google Crisis Map of the Philippines.

Screen Shot 2012-12-08 at 7.32.17 AM

One of my main priorities now is to make sure we do a far better job at leveraging advanced computing and microtasking platforms so that we are better prepared the next time we’re asked to repeat this kind of deployment. On the advanced computing side, it should be perfectly feasible to develop an automated way to crawl twitter and identify links to images  and videos. My colleagues at QCRI are already looking into this. As for microtasking, I am collaborating with PyBossa and Crowdflower to ensure that we have highly customizable platforms on stand-by so we can immediately upload the results of QCRI’s algorithms. In sum, we have got to move beyond simple crowdsourcing and adopt more agile micro-tasking and social computing platforms as both are far more scalable.

In the meantime, a big big thanks once again to all our digital volunteers who made this entire effort possible and highly insightful.

Summary: Digital Disaster Response to Philippine Typhoon

Update: How the UN Used Social Media in Response to Typhoon Pablo

The United Nations Office for the Coordination of Humanitarian Affairs (OCHA) activated the Digital Humanitarian Network (DHN) on December 5th at 3pm Geneva time (9am New York). The activation request? To collect all relevant tweets about Typhoon Pablo posted on December 4th and 5th; identify pictures and videos of damage/flooding shared in those tweets; geo-locate, time-stamp and categorize this content. The UN requested that this database be shared with them by 5am Geneva time the following day. As per DHN protocol, the activation request was reviewed within an hour. The UN was informed that the request had been granted and that the DHN was formally activated at 4pm Geneva.


The DHN is composed of several members who form Solution Teams when the network is activated. The purpose of Digital Humanitarians is to support humanitarian organizations in their disaster response efforts around the world. Given the nature of the UN’s request, both the Standby Volunteer Task Force (SBTF) and Humanity Road (HR) joined the Solution Team. HR focused on analyzing all tweets posted December 4th while the SBTF worked on tweets posted December 5th. Over 20,000 tweets were analyzed. As HR will have a blog post describing their efforts shortly (please check here), I will focus on the SBTF.

Geofeedia Pablo

The Task Force first used Geofeedia to identify all relevant pictures/videos that were already geo-tagged by users. About a dozen were identified in this manner. Meanwhile, the SBTF partnered with the Qatar Foundation Computing Research Institute’s (QCRI) Crisis Computing Team to collect all tweets posted on December 5th with the hashtags endorsed by the Philippine Government. QCRI ran algorithms on the dataset to remove (1) all retweets and (2) all tweets without links (URLs). Given the very short turn-around time requested by the UN, the SBTF & QCRI Teams elected to take a two-pronged approach in the hopes that one, at least, would be successful.

The first approach used  Crowdflower (CF), introduced here. Workers on Crowd-flower were asked to check each Tweet’s URL and determine whether it linked to a picture or video. The purpose was to filter out URLs that linked to news articles. CF workers were also asked to assess whether the tweets (or pictures/videos) provided sufficient geographic information for them to be mapped. This methodology worked for about 2/3 of all the tweets in the database. A review of lessons learned and how to use Crowdflower for disaster response will be posted in the future.

Pybossa Philippines

The second approach was made possible thanks to a partnership with PyBossa, a free, open-source crowdsourcing and micro-tasking platform. This effort is described here in more detail. While we are still reviewing the results of this approach, we expect that  this tool will become the standard for future activations of the Digital Humanitarian Network. I will thus continue working closely with the PyBossa team to set up a standby PyBossa platform ready-for-use at a moment’s notice so that Digital Humanitarians can be fully prepared for the next activation.

Now for the results of the activation. Within 10 hours, over 20,000 tweets were analyzed using a mix of methodologies. By 4.30am Geneva time, the combined efforts of HR and the SBTF resulted in a database of 138 highly annotated tweets. The following meta-data was collected for each tweet:

  • Media Type (Photo or Video)
  • Type of Damage (e.g., large-scale housing damage)
  • Analysis of Damage (e.g., 5 houses flooded, 1 damaged roof)
  • GPS coordinates (latitude/longitude)
  • Province
  • Region
  • Date
  • Link to Photo or Video

The vast majority of curated tweets had latitude and longitude coordinates. One SBTF volunteer (“Mapster”) created this map below to plot the data collected. Another Mapster created a similar map, which is available here.

Pablo Crisis Map Twitter Multimedia

The completed database was shared with UN OCHA at 4.55am Geneva time. Our humanitarian colleagues are now in the process of analyzing the data collected and writing up a final report, which they will share with OCHA Philippines today by 5pm Geneva time.

Needless to say, we all learned a lot thanks to the deployment of the Digital Humanitarian Network in the Philippines. This was the first time we were activated to carry out a task of this type. We are now actively reviewing our combined efforts with the concerted aim of streamlining our workflows and methodologies to make this type effort far easier and quicker to complete in the future. If you have suggestions and/or technologies that could facilitate this kind of digital humanitarian work, then please do get in touch either by posting your ideas in the comments section below or by sending me an email.

Lastly, but definitely most importantly, a big HUGE thanks to everyone who volunteered their time to support the UN’s disaster response efforts in the Philippines at such short notice! We want to publicly recognize everyone who came to the rescue, so here’s a list of volunteers who contributed their time (more to be added!). Without you, there would be no database to share with the UN, no learning, no innovating and no demonstration that digital volunteers can and do make a difference. Thank you for caring. Thank you for daring.

Help Tag Tweets from Typhoon Pablo to Support UN Disaster Response!

Update: Summary of digital humanitarian response efforts available here.

The United Nations Office for the Coordination of Humanitarian Affairs (OCHA) has just activated the Digital Humanitarian Network (DHN) to request support in response to Typhoo Pablo. They also need your help! Read on!


The UN has asked for pictures and videos of the damage to be collected from tweets posted over the past 48 hours. These pictures/videos need to be geo-tagged if at all possible, and time-stamped. The Standby Volunteer Task Force (SBTF) and Humanity Road (HR), both members of Digital Humanitarians, are thus collaborating to provide the UN with the requested data, which needs to be submitted by today 10pm 11pm New York time, 5am Geneva time tomorrow. Given this very short turn around time, we only have 10 hours (!), the Digital Humani-tarian Network needs your help!

Pybossa Philippines

The SBTF has partnered with colleagues at PyBossa to launch this very useful microtasking platform for you to assist the UN in these efforts. No prior experience necessary. Click here or on the display above to see just how easy it is to support the disaster relief operations on the ground.

A very big thanks to Daniel Lombraña González from PyBossa for turning this around at such short notice! If you have any questions about this project or with respect to volunteering, please feel free to add a comment to this blog post below. Even if you only have time tag one tweet, it counts! Please help!

Some background information on this project is available here.

Digital Humanitarian Response to Typhoon Pablo in Philippines

Update: Please help the UN! Tag tweets to support disaster response!

The purpose of this post is to keep notes on our efforts to date with the aim of revisiting these at a later time to write a more polished blog post on said efforts. By “Digital Humanitarian Response” I mean the process of using digital tech-nologies to aid disaster response efforts.


My colleagues and I at QCRI have been collecting disaster related tweets on Typhoon Pablo since Monday. More specifically, we’ve been collecting those tweets with the hashtags officially endorsed by the government. There were over 13,000 relevant tweets posted on Tuesday alone. We then paid Crowdflower workers to micro-task the tagging of these hash-tagged tweets based on the following categories (click picture to zoom in):


Several hundred tweets were processed during the first hour. On average, about 750 tweets were processed per hour. Clearly, we’d want that number to be far higher, (hence the need to combine micro-tasking with automated algorithms, as explained in the presentation below). In any event, the micro-tasking could also be accelerated if we increased the pay to Crowdflower workers. As it is, the total cost for processing the 13,000+ tweets came to about $250.

The database of processed tweets was then shared (every couple hours) with the Standby Volunteer Task Force (SBTF). SBTF volunteers (“Mapsters”) only focused on tweets that had been geo-tagged and tagged as relevant (e.g., “Casaualties,” “Infrastructure Damage,” “Needs/Asks,” etc.) by Crowdflower workers. SBTF volunteers then mapped these tweets on a Crowdmap as part of a training exercise for new Mapsters.

Geofeedia Pablo

We’re now talking with a humanitarian colleague in the Philippines who asked whether we can identify pictures/videos shared on social media that show damage, bridges down, flooding, etc. The catch is that these need to have a  location and time/date for them to be actionable. So I went on Geofeedia and scraped the relevant content available there (which Mapsters then added to the Crowdmap). One constraint of Geofeedia (and many other such platforms), however, is that they only map content that has been geo-tagged by users posting said content. This means we may be missing the majority of relevant content.

So my colleagues at QCRI are currently pulling all tweets posted today (Wed-nesday) and running an automated algorithm to identify tweets with URLs/links. We’ll ask Crowdflower workers to process the most recent tweets (and work backwards) by tagging those that: (1) link to pictures/video of damage/flooding, and (2) have geographic information. The plan is to have Mapsters add those tweets to the Crowdmap and to share the latter with our humanitarian colleague in the Philippines.

There are several parts of the above workflows that can (and will) be improved. I for one have already learned a lot just from the past 24 hours. But this is the subject of a future blog post as I need to get back to the work at hand.

Analyzing Disaster Tweets from Major Thai Floods

The 2011 Thai Floods was one of the country’s worst disasters in recent history.  The flooding began in July and lasted until December. Over 13 million people were affected. More than 800 were killed. The World Bank estimated $45 billion in total economic damage. This new study, “The Role of Twitter during a Natural Disaster: Case Study of 2011 Thai Flood,” analyzes how twitter was used during these major floods.

The number of tweets increase significantly in October, which is when the flooding reached parts of the Bangkok Metropolitan area. The month before (Sept-to-Oct) also a notable increase of tweets, which may “demonstrate that Thais were using Twitter to search for realtime and practical information that traditional media could not provide during the natural disaster period.”

To better understand the type of information shared on Twitter during the floods, the authors analyzed 175,551 tweets that used the hashtag #thaiflood. They removed “retweets” and duplicates, yielding a dataset of 64,582 unique tweets. Using keyword analysis and a rule based approach, the authors auto-matically classified these tweets into 5 categories:

Situational Announcements and Alerts: Tweets about up-to-date situational and location-based information related to the flood such as water levels, traffic conditions and road conditions in certain areas. In addition, emergency warnings from authorities advising citizens to evacuate areas, seek shelter or take other protective measures are also included.

Support Announcements: Tweets about free parking availability, free emergency survival kits distribution and free consulting services for home repair, etc.

Requests for Assistance: Tweets requesting any types of assistance; such as food, water, medical supplies, volunteers or transportation.

Requests for Information: Tweets including general inquiries related to the flood and flood relief such as inquiries for telephone numbers of relevant authorities, regarding the current situation in specific locations and about flood damage compensation.

Other: Tweets including all other messages, such as general comments; complaints and expressions of opinions.

The results of this analysis are shown in the figures below. The first shows the number of tweets per each category, while the second shows the distribution of these categories over time.

Messages posted during the first few weeks “included current water levels in certain areas and roads; announcements for free parking availability; requests for volunteers to make sandbags and pack emergency survival kits; announce-ments for evacuation in certain areas and requests for boats, food, water supplies and flood donation information. For the last few weeks when water started to recede, Tweet messages included reports on areas where water had receded, information on home cleaning andrepair and guidance regarding the process to receive flood damage compensation from the government.”

To determine the credibility of tweets, the authors identify the top 10 most re-tweeted users during the floods. They infer that the most retweeted tweets signal that the content of said tweets is perceived as credible. “The majority of these top users are flood/disaster related government or private organizations.” Siam Arsa, one of the leading volunteer networks helping flood victims in Thailand, was one of the top users ranked by retweets. The group utilizes social media on both Facebook  (www.facebook.com/siamarsa) and Twitter (@siamarsa) to share information about flooding and related volunteer work.”

In conclusion, “if the government plans to implement social media as a tool for disaster response, it would be well advised to prepare some measures or pro-tocols that help officials verify incoming information and eliminate false information. The  citizens should also be educated to take caution when receiving news and information via social media, and to think carefully about the potential effect before disseminating certain content.”

Gov Twitter

My QCRI colleagues and I are collecting tweets about Typhoon Pablo, which is making landfall in the Philippines. We’re specifically tracking tweets with one or more of the following hashtags: #PabloPh, #reliefPH and #rescuePH, which the government is publicly encouraging Filipinos to use. We hope to carry out an early analysis of these tweets to determine which ones provide situational aware-ness. The purpose of this applied action research is to ultimately develop a real-time dashboard for humanitarian response. This explains why we launched this Library of Crisis Hashtags. For further reading, please see this post on “What Percentage of Tweets Generated During a Crisis Are Relevant for Humanitarian Response?”

To Tweet or Not To Tweet During a Disaster?

Yes, only a small percentage of tweets generated during a disaster are directly relevant and informative for disaster response. No, this doesn’t mean we should dismiss Twitter as a source for timely, disaster-related information. Why? Because our efforts ought to focus on how that small percentage of informative tweets can be increased. What incentives or policies can be put in place? The following tweets by the Filipino government may shed some light.

Gov Twitter Pablo

The above tweet was posted three days before Typhoon Bopha (designated Pablo locally) made landfall in the Philippines. In the tweet below, the government directly and publicly encourages Filipinos to use the #PabloPH hashtag and to follow the Philippine Atmospheric, Geophysical & Astronomical Services Admin-istration (PAGASA) twitter feed, @dost_pagasa, which has over 400,000 follow-ers and also links to this official Facebook page.

Gov Twitter

The government’s official Twitter handle (@govph) is also retweeting tweets posted by The Presidential Communications Development and Strategic Plan-ning Office (@PCDCSO). This office is the “chief message-crafting body of the Office of the President.” In one such retweet (below), the office encourages those on Twitter to use different hashtags for different purposes (relief vs rescue). This mimics the use of official emergency numbers for different needs, e.g., police, fire, Ambulance, etc.

Twitter Pablo Gov

Given this kind of enlightened disaster response leadership, one would certainly expect that the quality of tweets received will be higher than without government endorsement. My team and I at QCRI are planning to analyze these tweets to de-termine whether or not this is the case. In the meantime, I expect we’ll see more examples of self-organized disaster response efforts using these hashtags, as per the earlier floods in August, which I blogged about here: Crowdsourcing Crisis Response following the Philippine Floods. This tech-savvy self-organization dynamic is important since the government itself may be unable to follow up on every tweeted request.