Anti-Vaccination Sentiments Spread More Easily Than Pro-Vaccination Sentiments
On Twitter, a popular microblogging and social-networking service, statements about vaccines may have unexpected effects -- positive messages may backfire, according to a team of Penn State researchers led by Marcel Salathé, an assistant professor of biology. The team tracked the pro-vaccine and anti-vaccine messages to which Twitter users were exposed and then observed how those users expressed their own sentiments about a new vaccine for combating influenza H1N1 -- a virus strain responsible for swine flu. The results, which may help health officials improve strategies for vaccination-awareness efforts, are published in the journal EPJ Data Science.
The researchers began by amassing all tweets with vaccination-related keywords and phrases during the 2009 H1N1 pandemic. They then tracked users' sentiments about the H1N1 vaccine. To sort through and categorize the tweets, Salathé's team asked Penn State students to rate a random subset of about 10 percent and 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.
Next, the team used the students' ratings to design a computer algorithm for cataloging the remaining 90 percent of the tweets according to the sentiments they expressed. "The human-rated tweets served as a 'learning set' that we used to 'teach' the computer how to rate the tweets accurately," Salathé explained. After the tweets were analyzed by the computer algorithm, the final tally was 318,379 tweets expressing either positive, negative or neutral sentiments about the H1N1 vaccine.
After categorizing the tweets, Salathé and his team then developed a statistical model with information including the number of microbloggers each Twitter user was following. In addition, the researchers recorded whether those followed microbloggers tended to tweet negatively or positively about the H1N1 vaccine. Also included in the model was the number of the negative or positive tweets each of the followed microbloggers sent out. "How many pro-vaccine or anti-vaccine individuals a Twitter user follows is just one measure. Also important is how many negative or positive tweets each followed microblogger then broadcasts to his followers," Salathé said. "It might be that a Twitter user follows only five anti-vaccine microbloggers, but if those five microbloggers all send 10 negative tweets per day, that might have an important impact." Other measures included in the statistical model were each Twitter user's number of reciprocal users -- how many pairs of microbloggers were following each other -- and the history of followers' own negative and positive tweets.
The team's first unexpected finding was that exposure to negative sentiment was contagious, while exposure to positive sentiments was not.
"Cause and effect are difficult to unravel in data such as these, so we can only speculate about why we saw this happen," Salathé said. "Whatever the reason, the observation is troubling because it suggests that negative opinions on vaccination may spread more easily than positive opinions."
The team's second unexpected finding was that microbloggers with more reciprocal Twitter relationships tended to be influenced differently depending on whether the vaccine sentiments of their connections were positive or negative.
Click here to read the full article: http://www.sciencedaily.com/releases/2013/04/130404122058.htm