Tag Archives: Tornado

Analysis of Multimedia Shared in Millions of Tweets After Tornado (Updated)

Posted on June 1, 2013 | 12 comments

Humanitarian organizations and emergency management offices are increasingly interested in capturing multimedia content shared on social media during crises. Last year, the UN Office for the Coordination of Humanitarian Affairs (OCHA) activated the Digital Humanitarian Network (DHN) to identify and geotag pictures and videos shared on Twitter that captured the damage caused by Typhoon Pablo, for example. So I’m collaborating with my colleague Hemant Purohit to analyze the multimedia content shared in the millions of tweets posted after the Category 5 Tornado devastated the city of Moore, Oklahoma on May 20th. The results are shared below along with details of a project I am spearheading at QCRI to provide disaster responders with relevant multimedia content in real time during future disasters.

Multimedia_Tornado

For this preliminary multimedia analysis, we focused on the first 48 hours after the Tornado and specifically on the following multimedia sources/types: Twitpic, Instagram, Flickr, JPGs, YouTube and Vimeo. JPGs refers to URLs shared on Twitter that include “.jpg”. Only ~1% of tweets posted during the 2-day period included URLs to multimedia content. We filtered out duplicate URLs to produce the following unique counts depicted above and listed below.

  • Twitpic = 784
  • Instagram = 11,822
  • Flickr = 33
  • JPGs = 347 
  • YouTube = 5,474
  • Vimeo = 88

Clearly, Instagram and Youtube are important sources of multimedia content during disasters. The graphs below (click to enlarge) depict the frequency of individual multimedia types by hour during the first 48 hours after the Tornado. Note that we were only able to collect about 2 million tweets during this period using the Twitter Streaming API but expect that millions more were posted, which is why access to the Twitter Firehose is important and why I’m a strong advocate of Big Data Philanthropy for Humanitarian Response.

Twitpic_Tornado

A comparison of the above Twitpic graph with the Instagram one below suggests very little to no time lag between the two unique streams.

Instagram_Tornado

Clearly Flickr pictures are not widely shared on Twitter during disasters. Only 53 links to Flickr were tweeted compared to 11,822 unique Instagram pictures.

Flickr_Tornado

The sharing of JPG images is more popular than links to Flickr but the total number of uniques still pales in comparison to the number of Instagram pictures.

JPGs_Tornado

The frequency of tweets sharing unique links to Youtube videos does not vary considerably over time.

Youtube_Tornado

In contrast to the large volume of Youtube links shared on twitter, only 88 unique links to Vimeo were shared.

Vimeo_Tornado

Geographic information is of course imperative for disaster response. We collected about 2.7 million tweets during the 10-day period after Tornado and found that 51.23% had geographic data—either the tweet was geo-tagged or the Twitter user’s bio included a location. During the first 48 hours, about 45% of Tweets with links to Twitpic had geographic data; 40% for Flickr and 38% for Instagram . Most digital pictures include embedded geographic information (i.e., the GPS coordinates of the phone or camera, for example). So we’re working on automatically  extracting this information as well.

An important question that arises is which Instagram pictures & Youtube videos actually captured evidence of the damage caused of the Tornado? Of these, which are already geotagged and which could be quickly geotagged manually? The Digital Humanitarian Network was able to answer these questions within 12 hours following the devastating Typhoon that ravaged the Philippines last year (see map below). The reason it took that long is because we spent most of the time customizing the microtasking apps to tag the tweets/links. Moreover, we were looking at every single link shared on twitter, i.e., not just those that linked directly to Instagram, Youtube, etc. We need to do better, and we can.

This is why we’re launching MicroMappers in partnership with the United Nations. MicroMappers are very user-friendly microtasking apps that allows anyone to support humanitarian response efforts with a simple click of the mouse. This means anyone can be a Digital Humanitarian Volunteer. In the case of the Tornado, volunteers could easily have tagged the Instagram pictures posted on Twitter. During Hurricane Sandy, about half-a-million Instagram pictures were shared. This is certainly a large number but other microtasking communities like my friends at Zooniverse tagged millions of pictures in a matter of days. So it is possible.

Incidentally, hundreds of the geo-tagged Instagram pictures posted during the Hurricane captured the same damaged infrastructure across New York, like the same fallen crane, blocked road or a flooded neighborhood. These pictures, taken by multiple eyewitnesses from different angles can easily be “stitched” together to create a 2D or even 3D tableau of the damage. Photosynth (below) already does this stitching automatically for free. Think of Photosynth as Google Street View but using crowdsourced pictures instead. One simply needs to a collection of related pictures, which is what MicroMappers will provide.

Photosynth

Disasters don’t wait. Another major Tornado caused havoc in Oklahoma just yesterday. So we are developing MicroMappers as we speak and plan to test the apps soon. Stay tuned for future blog post updates!

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See also: Analyzing 2 Million Disaster Tweets from Oklahoma Tornado [Link]

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

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Results: Analyzing 2 Million Disaster Tweets from Oklahoma Tornado

Posted on May 29, 2013 | 6 comments

Thanks to the excellent work carried out by my colleagues Hemant Purohit and Professor Amit Sheth, we were able to collect 2.7 million tweets posted in the aftermath of the Category 4 Tornado that devastated Moore, Oklahoma. Hemant, who recently spent half-a-year with us at QCRI, kindly took the lead on carrying out some preliminary analysis of the disaster data. He sampled 2.1 million tweets posted during the first 48 hours for the analysis below.

oklahoma-tornado-20

About 7% of these tweets (~146,000 tweets) were related to donations of resources and services such as money, shelter, food, clothing, medical supplies and volunteer assistance. Many of the donations-related tweets were informative in nature, e.g.: “As President Obama said this morning, if you want to help the people of Moore, visit [link]”. Approximately 1.3% of the tweets (about 30,000 tweets) referred to the provision of financial assistance to the disaster-affected population. Just over 400 unique tweets sought non-monetary donations, such as “please help get the word out, we are accepting kid clothes to send to the lil angels in Oklahoma.Drop off ”.

Exactly 152 unique tweets related to offers of help were posted within the first 48 hours of the Tornado. The vast majority of these were asking how to get involved in helping others affected by the disaster. For example: “Anyone know how to get involved to help the tornado victims in Oklahoma??#tornado #oklahomacity” and “I want to donate to the Oklahoma cause shoes clothes even food if I can.” These two offers of help are actually automatically “matchable”, making the notion of a “Match.com” for disaster response a distinct possibility. Indeed, Hemant has been working with my team and I at QCRI to develop algorithms (classifiers) that not only identify relevant needs/offers from Twitter automatically but also suggests matches as a result.

Some readers may be suprised to learn that “only” several hundred unique tweets (out of 2+million) were related to needs/offers. The first point to keep in mind is that social media complements rather than replaces traditional information sources. All of us working in this space fully recognize that we are looking for the equivalent of needles in a haystack. But these “needles” may contain real-time, life-saving information. Second, a significant number of disaster tweets are retweets. This is not a negative, Twitter is particularly useful for rapid information dissemination during crises. Third, while there were “only” 152 unique tweets offering help, this still represents over 130 Twitter users who were actively seeking ways to help pro bono within 48 hours of the disaster. Plus, they are automatically identifiable and directly contactable. So these volunteers could also be recruited as digital humanitarian volunteers for MicroMappers, for example. Fourth, the number of Twitter users continues to skyrocket. In 2011, Twitter had 100 million monthly active users. This figure doubled in 2012. Fifth, as I’ve explained here, if disaster responders want to increase the number of relevant disaster tweets, they need to create demand for them. Enlightened leadership and policy is necessary. This brings me to point six: we were “only” able to collect ~2 million tweets but suspect that as many as 10 million were posted during the first 48 hours. So humanitarian organizations along with their partners need access to the Twitter Firehose. Hence my lobbying for Big Data Philanthropy.

Finally, needs/offers are hardly the only type of useful information available on Twitter during crises, which is why we developed several automatic classifiers to extract data on: caution and advice, infrastructure damage, casualties and injuries, missing people and eyewitness accounts. In the near future, when our AIDR platform is ready, colleagues from the American Red Cross, FEMA, UN, etc., will be able create their own classifiers on the fly to automatically collect information that is directly relevant to them and their relief operations. AIDR is spearheaded by QCRI colleague ChaTo and myself.

For now though, we simply emailed relevant geo-tagged and time-stamped data on needs/offers to colleagues at the American Red Cross who had requested this information. We also shared data related to gas leaks with colleagues at FEMA and ESRI, as per their request. The entire process was particularly insightful for Hemant and I, so we plan to follow up with these responders to learn how we can best support them again until AIDR becomes operational. In the meantime, check out the Twitris+ platform developed by Amit, Hemant and team at Kno.e.sis. 

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See also: Analysis of Multimedia Shared on Twitter After Tornado [Link] 

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

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Over 2 Million Tweets from Oklahoma Tornado Automatically Processed (Updated)

Posted on May 22, 2013 | 9 comments

Update: We have now processed a total of 2 million tweets (up from 1 million).

My colleague Hemant Purohit at QCRI has been working with us on automatically extracting needs and offers of help posted on Twitter during disasters. When the 2-mile wide, Category 4 Tornado struck Moore, Oklahoma, he immediately began to collect relevant tweets about the Tornado’s impact and applied the algorithms he developed at QCRI to extract needs and offers of help.

tornado_ok

As long-time readers of iRevolution will know, this is an approach I’ve been advocating for and blogging about for years, including the auto-matching of needs and offers. These algorithms (classifiers) will also be made available as part of our Artificial Intelligence for Disaster Response (AIDR) platform. In the meantime, we have contacted our colleagues at the American Red Cross’s Digital Operations Center (DigiOps) to offer the results of the processed data, i.e., 1,000+ tweets requesting & offering help. If you are an established organization engaged in relief efforts following the Tornado, please feel free to get in touch with us (patrick@iRevolution.net) so we can make the data available to you. 

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

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