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Scottish Independence Social Media Analyses - some R tm ana Scottish Independence Social Media Analyses - some R tm ana

Scottish Independence Social Media Analyses - some R tm ana - PowerPoint Presentation

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Scottish Independence Social Media Analyses - some R tm ana - PPT Presentation

Dr Stephen Tagg Dr Mark Shepard Dr Stephen Quinlan HASSSBS University of Strathclyde Social Media Analysis Methods and Ethics Friday April 25 th 1050 This paper describes work done as part of a small ESRC project coding forum contributions and tweets ID: 459057

positive negative words analyses negative positive analyses words ethics number sentiment media social ref analysis coding identification methods tweets

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Slide1

Scottish Independence Social Media Analyses - some R tm analyses

Dr Stephen

Tagg

, Dr Mark Shepard, Dr Stephen Quinlan

HASS/SBS University of StrathclydeSlide2

Social Media Analysis: Methods and Ethics Friday April 25

th

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.Slide3

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 wordsSlide4

Sentiment Analysis

Slide5

General Inquirer

GraphSlide6

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

S

cottish (40% ) rather nationality unidentifiable (28%). Chi-square (over 6 categories )=583,

df

=5, p<.001. Slide7

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.