Washington: Marcel Salathe turned to Twitter to conduct a unique analysis of how the popular mircroblogging site records the spread of an epidemic or its users` receptivity to new vaccines.
Salathe, assistant professor of biology, Penn State University, then tracked how the users` thinking correlated with vaccination rates and how microbloggers with the same negative or positive feelings seemed to influence others.
Considered the first ever study in how social-media sites affect and reflect disease networks, the method could be utilised to track other diseases, reports the journal Public Library of Science Computational Biology.
Salathe explained that he chose Twitter for two reasons. First, unlike the contents of Facebook, Twitter messages or "tweets" are considered public data and anyone can "follow" or track the tweets of anyone else, according to a Penn statement.
Second, Twitter is the perfect database for learning about people`s sentiments. "Tweets are very short -- a maximum of 140 characters," Salathe explained.
"So users have to express their opinions and beliefs about a particular subject very concisely." Salathe began by amassing 477,768 tweets with vaccination-related keywords and phrases.
He then tracked users` sentiments about a particular new vaccine for combating H1N1 -- a virus strain responsible for swine flu. The collection began in August 2009, when news of the new vaccine first was made public, and continued through January 2010.
Salathe explained that sorting through the enormous number of vaccination-related tweets was no simple matter. First, he partitioned a random subset of about 10 percent and asked Penn State students to rate them as positive, negative, neutral or irrelevant.
For example, a tweet expressing a desire to get the H1N1 vaccine would be considered positive, while a tweet expressing the belief that the vaccine causes harm would be considered negative.
A tweet concerning a different vaccine, for example, the Hepatitis B vaccine, would be considered irrelevant.
After the tweets were analyzed by the computer algorithm, the final tally, after the irrelevant ones were eliminated, was 318,379 tweets expressing either positive, negative or neutral sentiments about the H1N1 vaccine.
Because Twitter users often include a location in their profiles, Salathe was able to categorise the expressed sentiments by the US region.
Using these data, Salathe found definite patterns. For example, the highest positive-sentiment users were from New England, and that region also had the highest H1N1 vaccination rate.
"The assumption is that people tend to communicate online almost exclusively with people who think the same way. This phenomenon creates `echo chambers` in which dissenting opinions are not heard," Salathe said. As it turned out, that assumption was correct.