NewsMiner Case Study
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When the UK Home Secretary, Alan Johnson, sacked his Government's drug adviser, Professor David Nutt, he was no doubt counting on the public's support following Nutt's controversial comments on the use and classification of cannabis. What he was unlikely to have been expecting was the reaction revealed by Spectrum's analysis of blogs and twitter on the story. The Spectrum analysis demonstrates the importance of monitoring and analysing social media such as blogs and twitter, not only because of their powerful influence but also because they reveal the sentiment of public opinion, which is not always reflected in online news.
Chart 2a (below) shows the 'co-occurrence', or association, between Alan Johnson and 'arrogance' in blogs covering the Professor Nutt story while chart 2b shows the co-occurrence, or association, between the Government and 'censorship' in the same blogs. Very importantly, this highlights a belief among many bloggers that while Nutt was wrong to make his remarks, the Government was also wrong to sack him because in doing so it was guilty of censorship and arrogance towards an unpaid adviser prepared to speak his mind.

Above charts show the number of times that key elements of the Professor Nutt story co-occur with each other in blogs. Co-occurrence provides a measure of association between terms, messages and brands. To download the full analysis scroll down to the examples at the foot of this page.
In twitter (chart 2c below), the main emphasis has been for Professor Nutt to be reinstated. As well as revealing the sentiment of users this also reflects the nature of tweets where the limited number of characters available for comment does not allow more detailed arguments to be developed. Many tweets therefore rely on a 'call to action' such as urging people to 'sign' online petitions or join Facebook groups in support of his reinstatement, as in the example shown at the bottom.

Above chart show the number of times that key elements of the Professor Nutt story co-occur with each other in twitter. Co-occurrence provides a measure of association between terms, messages and brands. To download the full analysis scroll down to the examples at the foot of this page.

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