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Scottish Independence Social Media Analyses - some R tm analyses

Scottish Independence Social Media Analyses - some R tm analyses. Dr Stephen Tagg , Dr Mark Shepard, Dr Stephen Quinlan HASS/SBS University of Strathclyde. Social Media Analysis: Methods and Ethics Friday April 25 th 10:50.

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Scottish Independence Social Media Analyses - some R tm analyses

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  1. Scottish Independence Social Media Analyses - some R tm analyses Dr Stephen Tagg, Dr Mark Shepard, Dr Stephen QuinlanHASS/SBS University of Strathclyde

  2. Social Media Analysis: Methods and Ethics Friday April 25th 10:50 • This paper describes work done as part of a small ESRC project coding forum contributions and tweets. • The R tm analyses have extended the hand coding using sentiment analysis and general inquirer tag codes, and have explored ways of automatically coding pro-independence and pro-union attitudes in tweets. • Future developments may allow the identification of tweet bots, to discover the relative usage by the yes and no campaigns.

  3. Sentiment Analyses • The following measures from (Bautin, Ward et al. 2010) are used in the sentiment plugin and attempt to produce comparable scores. • Polarity =(positive-negative)/(positive+negative) • Subjectivity= (positive+negative)/total number of words • Pos-refs per ref= positive/number of words • Neg refs per ref= Negative/ number of words • Senti-diffs-per ref= (positive-negative)/number of words

  4. Sentiment Analysis

  5. General Inquirer • Graph

  6. TwitterBot Identification • TwitterBots do not sleep, nor do they stop. They’re there to bias social media analyses • Identification by frequency and time patterns. Time patterns not available for Have Your Say discussions. • Those with more than 10 contributions are more likely to have been identified as Scottish (40% ) rather nationality unidentifiable (28%). Chi-square (over 6 categories )=583, df=5, p<.001.

  7. Summary/ Ethics • Ethics of those trying to use social media metrics – sold to indicate brand reputation • Ethics of using simple ‘bag of words’ methods rather than sophisticated machine learning – with more sensible language processing.

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