Second UK Election Debate

April 23 2010

There is a new view on the instant reactions made on Twitter about party leaders during the second televised election debate, April 22 2010. Preliminary results are published of the linguistic analysis of 169,000 tweets sent by 38,986 twitterers from 8.00pm – 9.30pm on the night of the debate.

Updated results for the analysis of 211,000 tweets sent by 47,420 twitterers from 8.30pm – 10pm on the night of the first UK election debate, April 15 2010, are also presented.

The overall tweet analysis (Figure 1) shows that for the second debate 43% of twitterers who expressed an opinion said that Nick Clegg performed best, down from 57% in the first debate, followed by Gordon Brown (35%, up from 25%), and then David Cameron (22%, up from 18%).
 

Figure 1: number of tweets showing positive sentiment towards each other

The analysis identified tweets saying that a particular leader was doing well or made a good point, or that they like the leader, etc. Linguistic filtering removed examples which were about expectations, e.g. “I hope the leader will do well”, questions, such as “anyone think the leader is doing well?”, and negations, such as “the leader did not do well” or “the leader made no sense”.
 

Figure 2: winner per topic from number of relevant positive tweets

The analysis identified a list of topics by identifying words or phrases which described the discussion subject, for example Trident, nuclear weapons, armed forces, military, and Eurofighter are assigned to defence. The tweets were then analyzed to find out who was saying positive things about each leader in relation to a specific topic.
 

Figure 3: percentage of specific topics won by each leader

This is an aggregation of all positive tweets about each leader with specific reference to any one of the topics. The same data is used for both Figure 2 and Figure 3.
 

Figure 4: number of times a topic is mentioned

This analysis is based on the transcript and not the tweets. As before, topics are not just a mention of a word, but bring together words or phrases which have similar meaning. It shows how important a particular topic was to a leader based on how many thimes they mentioned it during the debate.
 

Figure 5: postive sentiment towards leaders over time during the debate

This analysis shows how the positive tweets about each of the leaders varied during the course of the debate. There are number of peaks and troughs which relate to the topics being discussed during the debate.