anti-vaccination sentiments spread more easily than pro-vaccination sentiments

On Twitter, anti-vaccination sentiments spread more easily than pro-vaccination sentiments

April 4, 2013 by Katrina Voss

On Twitter, a research team tracked the
pro-vaccine and anti-vaccine messages about a new vaccine for combating a
virus strain responsible for swine flu, and then observed how Twitter
users expressed their own sentiments about the vaccine. The results may
help health officials improve strategies for vaccination-awareness
efforts. This image is a network diagram showing connected Twitter users
expressing negative (red) and positive (green) sentiments about
vaccination. Credit: Salathé lab, Penn State University



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 University
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,
will be published in the journal EPJ Data Science on 4 April 2013.









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 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 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."

Salathé hopes to design additional Twitter studies to test whether
the same effects can be observed for sentiments expressed about other
vaccines, as well as about other health issues such as antibiotic usage,
dieting, and exercising. "While some of our results from the H1N1 study
may seem frustrating, there are silver linings," Salathé said. "First,
we have a tried-and-true way to track and analyze the wealth of data out
there on Twitter. Second, further studies may reveal why positive
messages seem to encourage negative tweeting; perhaps there's something
about the manner in which the message is being conveyed. For example,
public health officials could use that information to send positive messages in a way that would be more likely to have the intended effect."

Source:-
http://phys.org/news/2013-04-twitter-anti-vaccination-sentiments-easily-pro-vaccination.html