Social Media, Disaster Response and the Streetlight Effect
A police officer sees a man searching for his coin under a streetlight. After helping for several minutes, the exasperated officer asks if the man is sure that he lost his coin there. The man says “No, I lost them in the park a few blocks down the street.” The incredulous officer asks why he’s searching under the streetlight. The man replies, “Well this is where the light is.”[1] This parable describes the “streetlight effect,” the observational bias that results from using the easiest way to collect information. The streetlight effect is an important criticisms leveled against the use of social media for emergency management. This certainly is a valid concern but one that needs to be placed into context.
I had the honor of speaking on a UN panel with Hans Rosling in New York last year. During the Q&A, Hans showed Member States a map of cell phone coverage in the Democratic Republic of the Congo (DRC). The map was striking. Barely 10% of the country seemed to have coverage. This one map shut down the entire conversation about the value of mobile technology for data collection during disasters. Now, what Hans didn’t show was a map of the DRC’s population distribution, which reveals that the majority of the country’s population lives in urban areas; areas that have cell phone coverage. Hans’s map was also static and thus did not convey the fact that the number cell phone subscribers increased by roughly 50% in the year leading up to the panel and ~50% again the year after.
Of course, the number of social media users in the DRC is far, far lower than the country’s 12.4 million unique cell phone subscribers. The map below, for example, shows the location of Twitter users over a 10 day period in October 2013. Now keep in mind that only 2% of users actually geo-tag their tweets. Also, as my colleague Kalev Leetaru recently discovered, the correlation between the location of Twitter users and access to electricity is very high, which means that every place on Earth that is electrified has a high probability of having some level of Twitter activity. Furthermore, Twitter was only launched 7 years ago compared to the first cell phone, which was built 30 years ago. So these are still early days for Twitter. But that doesn’t change the fact that there is clearly very little Twitter traffic in the DRC today. And just like the man in the parable above, we only have access to answers where an “electrified tweet” exists (if we restrict ourselves to the Twitter streetlight).
But this begs the following question, which is almost always overlooked: too little traffic for what? This study by Harvard colleagues, for example, found that Twitter was faster (and as accurate) as official sources at detecting the start and early progress of Cholera after the 2010 earthquake. And yet, the corresponding Twitter map of Haiti does not show significantly more activity than the DRC map over the same 10-day period. Keep in mind there were far fewer Twitter users in Haiti four years ago (i.e., before the earthquake). Other researchers have recently shown that “micro-crises” can also be detected via Twitter even though said crises elicit very few tweets by definition. More on that here.
But why limit ourselves to the Twitter streetlight? Only a handful of “puzzle pieces” in our Haiti jigsaw may be tweets, but that doesn’t mean they can’t complement other pieces taken from traditional datasets and even other social media channels. Remember that there are five times more Facebook users than Twitter users. In certain contexts, however, social media may be of zero added value. I’ve reiterated this point again in recent talks at the Council on Foreign Relation and the UN. Social media is forming a new “nervous system” for our planet, but one that is still very young, even premature in places and certainly imperfect in representation. Then again, so was 911 in the 1970′s and 1980′s as explained here. In any event, focusing on more developed parts of the system (like Indonesia’s Twitter footprint below) makes more sense for some questions, as does complementing this new nervous system with other more mature data sources such mainstream media via as GDELT as advocated here.
Read rest of post with additional graphics.
