Tweets and algorithms for saving lives (Al Jazeera)

Contrary to what seems to be the norm in Hollywood movies, people don't run in circles screaming and shouting when facing an emergency situation. The immediate, widespread, and ineffective mayhem so often portrayed in disaster movies is to a large extent a plot device, not very different from typical scenes in horror films in which people irrationally split and run straight into danger.

Sociologists of disaster, some of whom have researched these situations for decades, tell us a different story. When faced with a sudden crisis, people quickly try to gather as much information as they can from the sources most available in that moment: people around them, radio, television, or the internet. Based on this information, they evaluate the different alternatives, and take cover, flee, or act in a usually life-saving way. While panic can sometimes get in the way of safety, in most cases people's reactions are fast, calm, and more importantly, effective.

For example, in 2008, Qantas Flight 30 suffered an explosive decompression in midair due to a cargo door that "popped out", creating a hole the size of a small car. Passengers heard a loud noise, oxygen masks fell, and the aircraft rapidly started to drop in altitude to equalise air pressure. Little panic followed, and a passenger described the scene as: "No one panicked, there was no screaming. It was not your typical television movie. Everyone listened to the cabin staff."

People are not only effective saving their own lives, but also saving others. Most of the rescues in the immediate aftermath of a disaster are not done by fire brigades or professional emergency responders: It is the people directly affected by a disaster who take decisive actions and are indeed, the first responders.

With all this in mind, it is only natural that as social media spread and flourished in the past decade, it gradually took an important role in people's lives during emergencies, including natural and man-made disasters.

'Command and control' vs 'engage and listen'

Despite these realities, the "command and control" approach to disasters is fairly prevalent. In this framework, official authorities are expected to provide instructions to an uninformed and passive population. Indeed, this is the most common way in which social media is seen by government officials, as simply one more channel to push information out to the public.

While new and emerging volunteer organisations are often tech-savvy and native of online spaces, governments and formal non-governmental organisations that actually engage with and listen to affected populations through social media are still an exception rather than the norm. The American Red Cross was one of the pioneers, by creating a Digital Operations Center to monitor social media and to answer questions from the public, as well as disseminating life-saving information. The United Nation's Office for the Coordination of Humanitarian Affairs was another pioneer in the field which cofounded the Digital Humanitarian Network to extract information from social media to monitor a developing situation on cases of disasters.

At the government level, the disaster response strategy of both the Federal Emergency Management Agency (FEMA) in the US and the Philippines' government includes social media, the latter even chooses an "official" hashtag to be used for every large crisis event. At a more local level, the Twitter accounts offices for emergency management of both New York (@NYCOEM) and San Francisco (@SF_Emergency) often answer questions from the public through Twitter (my co-worker Patrick Meier has blogged extensively about these efforts, and similar initiatives).

The social media data deluge

Social media activity flares up in areas affected by disasters, often reaching up to thousands of postings and hundreds of photos per minute. Facebook data scientists have measured such bursts of activities during earthquakes. Others have even proposed (jokingly, but accurately) that tweets posted immediately after an earthquake come so fast, that in theory you could read a tweet about an earthquake before the seismic waves actually reach you.

The huge data volume and velocity makes it hard for everyone to make sense of social media data, but this is not the only problem. There are other concerns regarding the authenticity and veracity of messages, as social media is assumed to be less trustworthy than traditional media, mostly due to the anonymity users enjoy.

Also, rumours online are to a large extent self-correcting, and people question and correct social media news they consider dubious or false.

There are many problems with this assumption. In general, there is no reason to blindly trust everything anyone says, independently of whether it is online or offline, and independently of their credentials or performance in the past. Nobody is above making mistakes, including traditional media (such as CBS when it recently reported a "sideways tornado"). Particularly during emergencies, false rumours are often spread by well-intentioned people who simply weigh in the risk associated with not sharing potentially life-saving information, which may or may not end up being true.

The fact that many users share information without verifying it first may be a disadvantage of participative and social media, but it is also what makes social media so fast. Forbidding users from spreading "false news" can be dangerous in the face of a crisis, as it might also discourage them from spreading true news. In reality, being able to spread unverified information during an emergency is a key capacity of social media and one that can save lives. During a crisis, people don't take important decisions based on a single source, but instead contrast information from different sources. Also, rumours online are to a large extent self-correcting, and people question and correct social media news they consider dubious or false.

Crisis computing

Computational methods can contribute to rapidly filtering, sorting and aggregating vast volumes of social media during disasters. By a recent count, over 150 research articles have been published on algorithms for processing social media during crises.

These have focused on methods for collecting crisis-relevant data, detecting events and subevents, georeferencing information, determining information credibility, classifying information into categories, visualising the needs of affected populations in time and space, and even automatically generating summaries and timelines of a developing crisis from millions of postings - all this in the short time frame available during an emergency.

Interestingly, the key to a new wave of computational methods for processing social media data are people themselves. Hybrid methods combine human and machine intelligence by employing digital volunteers along with artificial intelligence (machine learning) methods. These methods are able to make sense of ambiguous data, something humans do much better than machines, as well as dealing with large volumes of data in a deterministic and reliable way, something machines do much better than humans.

For a researcher, to be able to use computer science to help in problems of societal value, such as emergency response and in general data science for social good, is a great opportunity and an invitation to participate in some of the most interesting challenges of applied computing.

The author wishes to thank research collaborators Muhammad Imran, Sarah Vieweg, Alexandra Olteanu, Hemant Purohit, Fernando Diaz and Patrick Meier.

Published in Al Jazeera: How tweets and algorithms can save lives »
December 5th, 2014.