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The first ever social media map to track the spread of epidemics has been created by an academic at Penn State University in America who analysed Twitter conversations about a new vaccine for combating swine flu.

Assistant professor of biology Marcel Salathe believes that his findings could be used to develop public health initiatives and help with the creation of targeted regional campaigns.

Salathe analysed almost 478,000 tweets with vaccination related keywords and phrases between August 2009, when a new vaccine was unveiled to combat H1N1, and January 2010.

He tracked how the users' attitudes correlated with vaccination rates and how microbloggers with the same negative or positive feelings seemed to influence others in their social circles.

Salathe said: 'People tweet because they want other members of the public to hear what they have to say. Tweets have to be very short...so users have to express their opinions and beliefs about a particular subject very concisely.'

Salathe partitioned a random subset of about ten per cent, and asked students to rate them as positive, negative, neutral or irrelevant. A tweet expressing a desire to get the H1N1 vaccine would be considered positive, while a tweet suggesting it was harmful would be considered negative. These ratings were then used in the design of a computer algorithm, which catalogued the remaining 90 per cent.

He added: 'The human-rated tweets served as a 'learning set' that we used to 'teach' the computer how to rate the tweets accurately.' The process also served as a filter, rejecting tweets that were irrelevant, leaving a final pool of almost 320,000 tweets for analysis.

Using location information from the tweets, Salathe was able to categorise sentiment by region. Using data from the Centers for Disease Control and Prevention (CDC), he was able to determine how attitudes correlated with the CDC's estimated vaccination rates.

He found that New England had the highest positive sentiment, and also the highest H1N1 vaccination rate. 'Targeted campaigns could be designed according to which region needs more prevention education,' said Salathe. 'Such data could also be used to predict how many doses of a vaccine will be required in a particular area.'

He also found that users with either negative or positive attitudes about the H1N1 vaccine followed like minded people. 'If anti-vaccination communities cluster in real, geographical space, as well, then this is likely to lead to under-vaccinated communities that are at great risk of local outbreaks,' said Salathe.

He now plans to use this map to study other diseases, including obesity. 'We think of a disease such as obesity as non-infectious, while a disease such as flu is clearly infectious. However, it might be more useful to think of behaviour-influenced diseases as infectious as well. Lifestyle choices might be 'picked up' in much the same way that pathogens - viruses or bacteria - are acquired.'

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