ts 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. Then, Shashank Khandelwal, a computer programmer and analyst in Penn State's Department of Biology and co-author of the paper, used the students' ratings to design a computer algorithm responsible 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," Salath explained. 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, Salath was able to categorize the expressed sentiments by U.S. region. Also, using data from the Centers for Disease Control and Prevention (CDC), he was able to determine how vaccination attitudes correlated with CDC- estimated vaccination rates. Using these data, Salath found definite patterns. For example, the highest positive-sentiment users were from New England, and that region also had the highest H1N1 vaccination rate. "These results could be used strategically to develop public-health initiatives," Salath explained. "For example, targeted campaigns could be designed according to which region needs more prevention education. Such data also could be used to predict how many doses of a vaccine will be required in a particular area."
In addition, Salath was able to construct an intricate social network by determining who followed the tweets of whom; that is, he was able to detePage: 1 2 3 4 Related biology news :1
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