So who really won the debate? Post-match analysis of public attitudes on Twitter

Immediately after the end of the leaders’ debate, media and political analysts rushed to identify the winners and losers of the event. Various exit polls were cited. Whereas YouGov proclaimed Nicola Sturgeon and Nigel Farage the winners, ICM put Miliband first. And today every part leader seems to celebrate his or her debate victory … of course. While the focus on the party leaders is understandable in the run-up to the election, we should perhaps pause for a minute and reflect back on the messages that were voiced yesterday; perhaps they could tell us a bit more of what ideas are likely to gain public support. Social Media could be useful in this respect. As we have already noticed (see the previous post on Democracy is Cyber-participation), the TV political debates seem to engage Twitter users. Using the Twitter streaming API to monitor ‘political’ tweets yesterday in real time, we recorded a massive rise in Twitter activity during the debate. The total count of ‘political’ tweets, that is, tweets including specific references to party terms and produced on Thursday 2nd April was 800,350, of which nearly 80% (614,800 tweets) were generated between 7pm and midnight. No doubt, Twitter users were engaging with the debate.

political tweets count

We were, however, interested in the ways in which Twitter users respond to the messages voiced by the individual party leaders and to what extent what was said by the party leaders influenced public attitudes or sentiments. In order to do so, we created a ‘political’ sentiment index. The index is based on evaluative words (mainly adjectives) retrieved from political tweets that we have been collecting over the last two weeks. Each item was given a score: +1 for positive meanings, -1 for negative meanings and 0 for neutral. When doing so, we recognised the fact that certain words may change their evaluative meanings when used in political contexts. Nevertheless, the massive amount of available data allows extracting valuable information even in the presence of semantic inaccuracies and noise. This is the beauty of the data-driven knowledge discovery.

Subsequently, a sentiment score was assigned to all the 600,000 political tweets generated during the debate. In this sense, our analysis is much more comprehensive that the one offered by Demos who considered only tweets which included boos and cheers. The graph below shows the moods in relation to political parties as the debate evolved. Four major topics were discussed including deficit, NHS, immigration and future for young people. The blue lines on the graphs below mark the time slots dedicated to each theme.


Sentiment_Major Parties

Twitter Sentiment Index

Sentiment_Other Parties

Twitter Sentiment Index

As can be seen, the support for each party fluctuated depending on the theme. Which messages scored particularly positively in the eyes of the public? NHS policy of Labour and LibDems seem to have scored well. 40 minutes into the debate, Ed Miliband outlines his plans on how to finance the NHS and following this statement, Labour reaches the peak of positive evaluation. Conversely, UKIP should seriously re-think its NHS policy; stigmatising HIV patients is not going to win public support, though UKIP’s views on immigration seemed to do the trick. SNP appears to be mostly positively evaluated. Having said that, certain messages seem to have been particularly endorsed. Nicola Sturgeon’s appeal for a rational debate on immigration (21:02) and her personal statement about free education that enabled her to be where she is (21:32) won massive support, as does her final statement, in which she outlined SNP as an alternative to Westminster.

The following two word-clouds have been generated with the frequent words found in the tweets associated with SNP and Nicola Sturgeon during the two main periods of Twitter popularity. These are the two periods with highest Political Sentiment Index and appear to have been inspired by Nicola’s key statements on immigration and education, respectively, at 20:55 and 21:35. And these are the messages that appeared to be the winners of the leaders’ debate.

Word Cloud1

Word-cloud for SNP tweets from 21:02 to 21:12

Word Cloud2

Word-cloud for SNP tweets from 21:40 to 22:00

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