Amy Sample Ward
FEBRUARY 19, 2011
My contribution to the panel is to provide context about the use of social media in emergency and disaster response as well as an overview of some of the tools we saw deployed last year and we may see in the future. Whether it’s direct or indirect content, why do citizens turn to social media in moments of need or disaster?
Amy Sample Ward
FEBRUARY 18, 2010
I have a guest post up on the NTEN blog discussing some of the ways social media was put into action as soon as the earthquakes struck in Haiti: When disaster strikes, we want information as soon as possible and we want to help just as quickly. But the way we think about and turn to social media in a time of disaster is changing.
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M+R
JANUARY 11, 2022
Overall, groups were split on whether they raised more or less overall digital revenue than end-of-year 2020 — in fact, our sample was split exactly 50/50! Help us map the whole year in data. Kinda like…2021. This includes all online revenue — ads, email, sms, etc. You might begin to believe there is no normal anymore. In 2021, same.
NTEN
FEBRUARY 17, 2010
Amy Sample Ward, NetSquared. When disaster strikes, we want information as soon as possible and we want to help just as quickly. But the way we think about and turn to social media in a time of disaster is changing. Be sure to visit the site and press "Play" on the map. How can we do that?
Beneblog: Technology Meets Society
MARCH 17, 2011
Here's the bottom line: if you have a map of buildings from before the earthquake, you already know more about the likely location of damaged buildings than if you relied on an SMS stream, based on the Haiti data presented.
Byte Technology
OCTOBER 13, 2016
Because they can detect nuances in plantings that can’t be viewed effectively from the ground—healthy densities for example—they can help farmers plan ahead to avoid disasters arising from droughts, infections that can wipe out entire yields and even whether or not certain sections of plantings are maturing at a faster rate than others.
Beneblog: Technology Meets Society
MARCH 28, 2011
We’re reacting to the following claim: “Data collected using unbounded crowdsourcing (non-representative sampling) largely in the form of SMS from the disaster affected population in Port-au-Prince can predict, with surprisingly high accuracy and statistical significance, the location and extent of structural damage post-earthquake.”
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