2,412 crowdsourcing and crowdfunding sites
Editor's Note: The following is reprinted with permission from the author, Patrick Meier. It was originally published at iRevolution.net. Follow Patrick on Twitter @patrickmeier. Also see Patrick's most recent article on this site about crowd-powered Hurricane Sandy recovery.
Monitoring social media for digital humanitarian response can be a massive undertaking. The sheer volume and velocity of tweets generated during a disaster makes real-time social media monitoring particularly challenging if not near impossible. However, two new studies argue that there is “a better way to track the spread of information on Twitter that is much more powerful.”
Manuel Garcia-Herranz and his team at the Autonomous University of Madrid in Spain use small groups of “highly connected Twitter users as ‘sensors’ to detect the emergence of new ideas. They point out that this works because highly connected individuals are more likely to receive new ideas before ordinary users.” To test their hypothesis, the team studied 40 million Twitters users who “together totted up 1.5 billion follows’ and sent nearly half a billion tweets, including 67 million containing hashtags.”
They found that small groups of highly connected Twitter users detect “new hashtags about seven days earlier than the control group. In fact, the lead time varied between nothing at all and as much as 20 days.” Manuel and his team thus argue that “there’s no point in crunching these huge data sets. You’re far better off picking a decent sensor group and watching them instead.” In other words, “your friends could act as an early warning system, not just for gossip, but for civil unrest and even outbreaks of disease.”
The second study, “Identifying and Characterizing User Communities on Twitter during Crisis Events,” (PDF) is authored by Aditi Gupta et al. Aditi and her colleagues analyzed three major crisis events (Hurricane Irene, Riots in England and Earthquake in Virginia) to “to identify the diﬀerent user communities, and characterize them by the top central users.” Their findings are in line with those shared by the team in Madrid. “[T]he top users represent the topics and opinions of all the users in the community with 81% accuracy on an average.” In sum, “to understand a community, we need to monitor and analyze only these top users rather than all the users in a community.”
How could these findings be used to prioritize the monitoring of social media during disasters? See this blog post for more on the use of social network analysis for humanitarian response.
- Patrick is an internationally recognized thought leader on the application of new technologies for crisis early warning, humanitarian response and resilience. He currently serves as Director of Social Innovation at the Qatar Computing Research Institute (QCRI) where he develops next-generation humanitarian technologies by leveraging Big Data Analytics, Artificial Intelligence, Machine Learning and Social Computing. Patrick holds a PhD from The Fletcher School, a Pre-Doctoral Fellowship from Stanford and an MA from Columbia University. He was born & raised in Africa.