How tweets and algorithms can save lives

Social media is increasingly becoming an important tool for
emergency management and response .
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 mid -air 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 co-founded the Digital Humanitarian
Network to extract information from social media to monitor
a developing situation om cases of disasters .
At the government level, the disaster response strategy of
both the Federal Emergency Management ( 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.
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 .
Carlos Castillo is a Senior Scientist at the Qatar
Computing Research Institute in Doha. He is a web miner
with a background in information retrieval, and has been
influential in the areas of adversarial web search and web
content quality and credibility .
Follow him on Twitter : @ ChaToX