As the global Ebola crisis con­tinues to evolve, each day brings new updates and news on the sit­u­a­tion, from health offi­cials inves­ti­gating a poten­tial new case in the U.S. to gov­ern­ment leaders dis­cussing response plans.

Not sur­pris­ingly, Twitter has been a pri­mary source for people to follow and dis­cuss the latest Ebola news, but “there have been times when it’s felt like if you’re away from the Internet for three hours, you come back and it’s dif­fi­cult to under­stand every­thing that’s going on,” said North­eastern net­work sci­en­tist Alessandro Vespig­nani, the Stern­berg Family Dis­tin­guished Pro­fessor of Physics and who holds joint appoint­ments in the Col­lege of Sci­ence, the Col­lege of Com­puter and Infor­ma­tion Sci­ence, and the Bouvé Col­lege of Health Sci­ences.

This has moti­vated Vespignani’s team in Northeastern’s MoBS lab—in col­lab­o­ra­tion with the Italy-​​based ISI Foun­da­tion, the Insti­tute for Sci­en­tific Interchange—to create Ebo­la­Tracking, a Web tool that allows the public to follow in real time all the latest news and Twitter dis­cus­sion on Ebola.

The “sit­u­a­tional aware­ness tool,” as Vespig­nani calls it, pulls men­tions of Ebola and related key­words from Twitter and dis­plays them on an inter­ac­tive world map. The map groups tweets by loca­tion, whether that loca­tion is men­tioned in the tweet or is from a news outlet in that loca­tion. It also fil­ters out the junk and spam tweets.

Ebo­la­Tracking is a Web inter­face that lever­ages Twitter data,” he explained. “Twitter is very quick; it picks up on sig­nals right away, so we decided to con­struct a plat­form that is able to dis­till these sig­nals in a mean­ingful way.”

Vespig­nani stressed that the tool only tracks news and dis­cus­sions about Ebola on Twitter based on loca­tion; it doesn’t track the virus or pre­dict where it will spread.

How­ever, his team’s ongoing research at the MoBS lab is focused on that. For months now the lab has been tracking the epi­demic in West Africa and its poten­tial to spread glob­ally. The team has devel­oped a cutting-​​edge com­pu­ta­tional model using Big Data that allows them to visu­alize the spread of Ebola, a project com­bines real-​​world pop­u­la­tion and human mobility data with elab­o­rate models on dis­ease trans­mis­sion.  The team has used these models to pre­dict the pro­gres­sion of Ebola and is reg­u­larly updating its pre­dic­tion assess­ments to reflect new trends in the disease’s spread as more infor­ma­tion becomes available.