Anti-vaccination statements spread faster on Twitter than pro-vaccination statements
Marcel Salathé, an assistant professor of biology at Penn State, and his colleagues amassed all tweets and vaccination-related phrases and keywords related to the 2009 H1N1 pandemic and asked Penn State students to rate a random subset of the tweets as positive, negative, neutral or irrelevant. The team used the students' ratings to create a computer algorithm to rate 318,379 tweets about the H1N1 vaccine.
The team found that exposure to negative sentiment was much more contagious than positive sentiment.
"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 also found that Twitter users with more reciprocal relationships on the social networking platform and a negative sentiment about the vaccine tended to tweet more opinions about the vaccine. Users with pro-vaccine sentiments did not encourage others to tweet additional positive sentiments.
Salathé's team also found that pro-vaccine messages tended to backfire.
"Not surprisingly, we found that a high volume of negative tweets seemed to encourage people to tweet more negatively," Salathé said. "But strangely, a high volume of positive tweets seemed to encourage people to tweet more negatively, too. In other words, pro-vaccine messages seemed to backfire when enough of them were received."
Salathé said the study could help public health officials to create more effective pro-vaccine campaigns in the future.
"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."
Salathé plans to design additional Twitter studies to test if the same pattern holds true for other vaccines, dieting, exercising and antibiotic usage.
The results of the study were published in EPJ Data Science.