Tag Archives: Youtube

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!

bio

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

12 Comments

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

Tagged , , , , ,

GeoFeedia: Ready for Digital Disaster Response

Posted on March 24, 2013 | 16 comments

GeoFeedia was not originally designed to support humanitarian operations. But last year’s blog post on the potential of GeoFeedia for crisis mapping caught the interest of CEO Phil Harris. So he kindly granted the Standby Volunteer Task Force (SBTF) free access to the platform. In return, we provided his team with feedback on what features (listed here) would make GeoFeedia more useful for digital disaster response. This was back in summer 2012. I recently learned that they’ve been quite busy since. Indeed, I had the distinct pleasure of sharing the stage with Phil and his team at this superb conference on social media for emergency management. After listening to their talk, I realized it was high time to publish an update on GeoFeedia, especially since we had used the tool just two months earlier in response to Typhoon Pablo, one of the worst disasters to hit the Philippines in the past 100 years.

The 1-minute video is well worth watching if you’re new to GeoFeedia. The plat-form enables hyper local searches for information by location across multiple social media channels such as Twitter, Youtube, Flickr, Picasa & now Instagram. One of my favorite GeoFeedia features is the awesome geofeed (digital fence), which you can learn more about here. So what’s new besides Instagram? Well, the first suggestion I made last year was to provide users with the option of searching by both location and topic, rather than just location alone. And presto, this now possible, which means that digital humanitarians today can zoom into a disaster-affected area and filter by social media type, date and hashtag. This makes the geofeed feature even more compelling for crisis response, especially since geofeeds can also be saved and shared.

The vast majority of social media monitoring tools out there first filter by key-word and hashtag. Only later do they add location. As Phil points out, this mean they easily miss 70% of hyper local social media reports. Most users and org-anizations, who pay hefty licensing fees to uses these platforms, are typically unaware of this. The fact that GeoFeedia first filters by location is not an accident. This recent study (PDF) of the 2012 London Olympics showed that social media users posted close to 170,000 geo-tagged to Twitter, Instagram, Flickr, Picasa and YouTube during the games. But only 31% of these geo-tagged posts contained any Olympic-specific keywords and/or hashtags! So they decided to analyze another large event and again found the number of results drop by about 70% when not first filtering by location. Phil argues that people in a crisis situation obviously don’t wait for keywords or hashtags to form; so he expects this drop to happen for disasters as well. “Traditional keyword and hashtag search thus be complemented with a geo-graphical search in order to provide a full picture of social media content that is contextually relevant to an event.”

Screen Shot 2013-03-23 at 4.42.25 PM

One of my other main recommendations to Phil & team last year had to do with analytics. There is a strong need for an “Analytics function that produces summary statistics and trends analysis for a geofeed of interest. This is where Geofeedia could better capture temporal dynamics by including charts, graphs and simple time-series analysis to depict how events have been unfolding over the past hour vs 12 hours, 24 hours, etc.” Well sure enough, one of GeoFeedia’s major new features is a GeoAnalytics Dashboard; an interface that enables users to discover temporal trends and patterns in social media—and to do so by geofeed. This means a user can now draw a geofeed around a specific area of interest in a given disaster zone and search for pictures that capture major infrastructure damage on a specified date that contain tags or descriptions with the words “#earthquake”, “damage,” “buildings,” etc. As Phil rightly points out, this provides a “huge time advantage during a crisis to give a yet another filtered layer of intelligence; in effect, social media that is highly relevant and actionable ‘bubbling-up to the top’ of the pile.” 

Analytics Screen Shot - CES Data

I truly am a huge fan of the GeoFeedia platform. Plus, Phil & team have been very responsive to our interests in using their tool for disaster response. So I’m ex-cited to see which features they build out next. They’ve already got a “data portability” functionality that enables data export. Users can also publish content from GeoFeedia directly to their own social networks. Moreover, the filtered content produced by geofeeds can also be shared with individual who do not have a GeoFeedia account. In any event, I hope the team will take into account two items from my earlier wish list—namely Sentiment Analysis and GeoAlerts.

A Sentiment Analysis feature would capture the general mood and sentiment  expressed hyper-locally within a defined geofeed in real-time. The automated Geo-Alerts feature would make the geofeed king. A GeoAlerts functionality would enable users to trigger specific actions based on different kinds of social media traffic within a given geofeed of interest. For example, I’d like to be notified if the number of pictures posted within my geofeed that are tagged with the words “#earthquake” and “damage,” increases by more than 20% in any given hour. Similarly, one could set a geofeed’s GeoAlert for a 10% increase in the number of tweets with the words “cholera” and “diarrhea” (these need not be in English, by the way) in any given 10-minute period. Users would then receive GeoAlerts via automated emails, Tweets and/or SMS’s. This feature would in effect make the GeoFeedia more of a mobile and “hands free” platform, like Waze for example.

My first blog post on GeoFeedia was entitled “GeoFeedia: Next Generation Crisis Mapping Technology?” The answer today is a definite “Yes!” While the platform was not originally designed with disaster response in mind, the team has since been adding important features that make the tool increasingly useful for humanitarian applications. And GeoFeedia has plans for more exciting develop-ments in 2013. Their commitment to innovation and strong continued interest in supporting digital disaster response is why I’m hoping to work more closely with them in the years to come. For example, our AIDR (Artificial Intelligence for Disaster Response) platform would really add a strong Machine Learning com-ponent to GeoFeedia’s search function, in effect enabling the tool to go beyond simple keyword search.

Bio

16 Comments

Posted in Crisis Mapping, Crowdsourcing, Humanitarian Technologies, Social Media

Tagged , , , , , , , ,

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

Posted on April 17, 2012 | 13 comments

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

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

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

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

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

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

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

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

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


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

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

13 Comments

Posted in Crisis Mapping, Crowdsourcing, Early Warning, Humanitarian Technologies, Social Computing, Social Media

Tagged , , , , , , , , , , , , , ,

Gene Sharp, Civil Resistance and Technology

Posted on December 25, 2008 | 18 comments

Major civil nonviolent campaigns are twice as likely to lead to sustainable democratic transitions than violent campaigns. This conclusion comes from a large-N statistical study carried out by my colleague Maria Stephan (PhD Fletcher ’06) and Erica Chenoweth. Recently published in International Security, the study notes that civil resistance movements have achieved success 55% of the time while only 28% of violent campaigns have succeeded.

Another colleague, Chris Walker (MALD Fletcher ’07), wrote in his excellent Master’s Thesis that “techniques associated with strategic nonviolent social movements are greatly enhanced by access to modern information communication technologies, such as mobile telephony, short message service (SMS), email and the World Wide Web, among others.”

It stands to reason, then, that increasing access to modern communication technologies may in turn up the 55% success rate of nonviolent campaigns by several percentage points. To this end, the question that particularly interests me (given my dissertation research) is the following: What specific techniques associated with civil resistance can tactical uses of modern communication technologies amplify?

This is the question I recently posed to Dr. Peter Ackerman—another Fletcher Alum (PhD ’76) and the founding Chair of the International Center for Nonviolent Conflict (ICNC)—when I described my dissertation interests. When Peter suggested I look into Gene Sharp’s work on methods of nonviolent action, I replied “that’s exactly what I intend to do.”

In The Politics of Nonviolent Action, Gene identifies 198 methods of nonviolent protest and persuasion. The majority of these can be amplified by modern communication technologies. What  follows is therefore only a subset of 12 tactics linked to applied examples of modern technologies. I very much welcome feedback on this initial list, as I’d like to formulate a more complete taxonomy of digital resistance and match the tactic-technologies with real-world examples from DigiActive’s website.

  • Quickie walkout (lightning strike): Flashmob
  • Hiding, escape, and false identities: Mobile phone, SMS

Do please let me know (in the comments section below) if you can think of other communication technologies, Web 2.0 applications, examples, etc. Thanks!

Patrick Philippe Meier

18 Comments

Posted in Digital Activism

Tagged , , , , , , , , , , , , , , , , ,