The University of Reading’s workshop on “Big Social Data: Interdisciplinary Analytics”

The University of Reading’s workshop on “Big Social Data: Interdisciplinary Analytics” was held last week, with funding contributions from the University’s RETF and the Dept. of English and Applied Linguistics (DELAL), Henley Business School (HBS), School of Politics, Economics and International Relations (SPEIR) and School of Systems Engineering (SSE). The workshop was organised by academics […]

The BBCDebate: absentees more influential?

Last night the BBC aired its debate of the challengers, as it put it, with leaders of the five opposition parties squaring up to each other. Prime Minister David Cameron and Deputy Prime Minister Nick Clegg did not participate, and the latter was at pains to point out that he wasn’t even invited.

There’s little doubt this wasn’t the biggest Twitter event of the election campaign, but nonetheless well over a thousand tweets per minute were recorded, and in total we collected 151,417 tweets surrounding the event. Most activity, understandably, came towards the end of the debate as each politician tried to leave viewers with their version of events:

Number of tweets per minute

The spike towards the end could perhaps be explained away by the three “major” parties going into spinning overdrive as the debate closed; this seems clearer looking at the numbers of tweets per party:

Number of tweets per party

The second Ukip spike, just after 8:30pm, appears to coincide with Nigel Farage’s attack on the audience both in the studio and at home, while nearer 9pm is when the debate moved to immigration; at this point Ukip were getting more than twice as many mentions on Twitter as any other party.

As Sylvia outlined in our last post after the seven-way debate, we’ve created out own sentiment index, and below we plot the index for each of the parties, including the two not participating in the debate:

Sentiment during #BBCDebate

 

What is perhaps most notable is that the index with the biggest range is the Conservative one, despite David Cameron not participating; just before 9, not long after the question on defence, Conservative sentiment is at rock bottom, but just before the end of the debate (perhaps co-ordinated?), Tory sentiment is soaring, although in the final minute Labour’s sentiment is almost identical. The SNP, widely noted for their social media campaigning, also show a late burst, although Sturgeon’s somewhat disappointing final comments appear reflected in the last minute tail off in sentiment.

Overall it’s clear that very little is clear regarding who “won” last night, and whether indeed it was one of the two parties that didn’t participate – at least in the televised debate…

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

Racism, Farage and Clarkson

The political story of the last 24 hours is clear: Ukip’s leader Nigel Farage would scrap racial discrimination laws in order to set free our employers from the shackles that bind them. Regardless of one’s feelings about this (Fraser Nelson thinks the SNP’s anti-rich bigotry is more appalling, while naturally the Huffington Post takes a different line), there’s little doubt it’s driven much content on social media in the last 24 hours; in the last hour alone, over a thousand tweets specifically mentioning Ukip have been sent.

Here in Reading we’re collecting election-related Tweets, and so this seemed like an good opportunity to visualise what’s going on. Below is a word cloud composed of two types of words: firstly terms in green, such as party names and references, and other proper nouns, and the second set if plain old words, and how frequently they occur. The font size is dictated by the frequency of the word or term: bigger for more commonly found terms.

Wordcloud 12 March

Unsurprisingly Ukip figure prominently in the terms, but amongst the words we see: legislation, racist, racial, discrimination, equality, scrap, rid, laws, and Nigel.

One interesting word there is misrepresent; it’s often claimed that Ukip are misrepresented – could that be what’s happening here?

Another term, tucked away in small font is one that keeps rumbling along: Clarkson.