Statements about vaccines have unexpected effects, with positive messages sometimes backfiring on Twitter, a popular social networking site.

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 5 anti-vaccine microbloggers, but if those 5 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. "We found that, in reciprocal microblogging relationships, negative sentiments were more socially contagious than positive sentiments," Salathé said. "When a microblogger had a lot of reciprocal Twitter connections with users who expressed anti-vaccine sentiments, he tended to tweet even more anti-vaccine sentiments himself." Interestingly, however, Salathé and his team found that the same did not hold true for microbloggers with reciprocal connections with users who expressed pro-vaccine sentiments; that is, pro-vaccine sentiments did not seem to encourage people to tweet more positive sentiments of their own.
"Our third finding was the most bizarre and perhaps the most discouraging," Salathé said. He and his team looked at the sheer volume of negative or positive tweets followers received -- independent of how many individuals the users followed. "Not surprisingly, we found that a high volume of negative tweets seemed to encourage people to tweet more negatively. But strangely, a high volume of positive tweets seemed to encourage people to tweet more negatively, too," Salathé said. "In other words, pro-vaccine messages seemed to backfire when enough of them were received."
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