BBC Sports Personality of the Year results summary

So the winner of the BBC Sports Personality concurred with the overall analysis that we undertook, which was Tony McCoy. As we stated yesterday, Tony McCoy was consistently at the top of the analysis that we did, based on tweets made during the past week.

The bookies generally had Graeme McDowell as second favourite, although in our analysis he was well down the list along with fellow golfer Lee Westwood.

The second placed person was Phil Taylor, who trended upwards in our analysis as the week wore on. He actually finished top of the list of the analysis of tweets made during the show, indicating a late swing towards him. However this wasn't sufficient to topple Tony McCoy but moved him into second position. In fact using our contextual analysis we can examine why this is the case. There are a number of words in close context to Phil Taylor in a negative context such as: superfit, insult, athletes, skill. This indicates that people do not think that there is great skill to darts, and that should he win it would be an insult to real athletes. There is also a great deal of negative comment on the fact that Phil Taylor does not exactly have to be 'superfit' to be able to win his championships. This gives an insight into why Phil Taylor didn't win.

In third position was Jessica Ennis, which let's be honest does not fit well with our analysis. Although she was shown in third position in our analysis of tweets made during the show, she was behind Amy Williams. In fact, in our analysis she consistently lagged behind Amy throughout. This is probably due to a couple of reasons, the main one being that the demographic that voted for her is not well represented on twitter.

The higher position of Amy Williams and Mark Cavendish in our list may also be due to twitter campaigns by followers of the respective sportsperson. Since twitter is a 'free' utility, it costs nothing for groups of people to retweet something along the lines of "Amy to win #spoty!" in order to try to influence other people's votes. We have seen this previously with analysis done on shows like the X Factor.

So overall, our analysis was able to pick the winner, and also showed trending for Phil Taylor in particular.

BBC Sports Personality of the Year prediction during show

The results of the analysis of tweets made during the TV show tonight can be seen below:

1. Phil Taylor
2. Amy Williams
3. Jessica Ennis
4. Tom Daley
5. Tony McCoy
6. Mark Cavendish
7. David Haye
8. Lee Westwood
9. Graeme McDowell
10. Graeme Swann

BBC Sports Personality of the Year prediction pre-show

We have analysed the tweets made during today in the run up to the show itself. Our cut-off is 19:00, the time at which the show starts and the phone lines are announced so that the public can begin to vote for their favourite. We will keep an eye on when the lines will close but will look to close our 'tweet vote line' at about 8:30pm, to ensure our analysis goes up before the announcement of the winner.

In any case, the ordering based on the tweets so far today is:

1. Tony McCoy
2. Mark Cavendish
3. Tom Daley
4. Phil Taylor
5. Amy Williams
6. Jessica Ennis
7. Lee Westwood
8. David Haye
9. Graeme McDowell
10. Graeme Swann

It is perhaps not surprising to see Graeme Swann at the bottom of the list, given the abject performance by England in the Ashes this last weekend.  However his namesake Graeme McDowell is rather more of a surprise, particularly as Lee Westwood appears ahead of him in the list.

Another surprise is to see Mark Cavendish at number 2 in the list.  He has been up and down the ranking this week, perhaps reflecting a concerted twitter campaign by his key fans to try to garner extra votes.  In fact, our experience from the X Factor analysis has shown how this can easily bias the results.  However, in the X Factor, we were able to analyse the data to see that often there was a strong negative bias coming through the data, relating to the fact that people on twitter were reacting to the campaign, and basically stating that they did *not* want to see that person win.  We see a similar effect with two individuals so far - Tony McCoy and Mark Cavendish.  This would appear to suggest that some people at least are reacting to the campaigns to promote these two sportsmen - there has been a concerted effort by the gambling and horse racing community to promote Tony McCoy - and that this may count against them in the final reckoning?

Look out for our next update in just over an hours time when we will analyse the tweets made during the broadcast.

BBC Sports Personality of the Year prediction 19th December

We have been tracking twitter in order to give an indication as to what the public are thinking with regards to the BBC Sports Personality of the year. Again we are keeping track of every tweet that is being made in relation to this topic, and using our contextual analysis method to try to ascertain who is the most likely to win, and to also give further insight into the reasons behind the public's voting intentions.

We have been tracking the tweets for the past week and the ranking of the contestants over this past week can be seen in the figure below:



The graph shows that Tony McCoy has been the favourite for most of the week, but that there has been some changes in the last few days. Based on yesterday's data, he has dropped down with Phil Taylor and David Haye in particular having huge surges of tweets about them. However, unlike other reality shows, the public can only vote during the show itself. We will therefore look to analyse the tweets made during the show to give an indication of who is looking the most likely.

Based on all the tweets gathered thus far, Tony McCoy is still the overrall favourite to win, with Amy Williams likely to be the winning female contestant.

We will update the analysis during the show.

Strictly Come Dancing post result analysis

Our analysis last night during the first part of the Strictly Come Dancing show indicated that although Matt was the favourite individual contestant, that Kara and Artem as a couple were more favoured, and that this could influence the final outcome of the competition.

Unfortunately we had not realised that there was to be a second window for voting, and so were unable to present the results of tweets made during this second show at the time (we wrongly thought it was a straight forward results show). However we can look back now and see what the analysis of the tweets made between 21:05 and 22:00 last night tells us (i.e. after Pamela was told she was in 3rd position).

The analysis shows a huge trend towards Kara, and this is highlighted in the graph shown below:



This graph shows the trend of votes over the course of the past week, and shows the proportion of the score for each contestant when compared against each other (the person with the highest score has 100%). Initially, and up until the show itself last night, Matt was the clear favourite, with Kara and Pamela, level pegging behind him. Then during the course of the 1st show last night, Kara experienced a surge in popularity, taking her to an equivalent score as Matt, leaving Pamela behind. This surge of votes resulted in Pamela being voted out of the show in 3rd place.

There then followed a 2nd show, where the voting lines were re-opened. Both contestants had a further dance, and we can see that with Pamela out of the show, the vast majority of votes appears to have transferred directly to Kara - her score catapults above Matt's and shows her to be the clear winner, based on twitter data.

This gives us an insight into the voting intentions of the public, and perhaps indicates that despite Matt being the favourite all week, he had very little chance of winning since Kara's votes may well have transferred to Pamela had she been placed in 3rd.

In addition, we can see that the trend for the dancing partners was an important factor in the outcome for this year's competition. Aliona received only 30% of the score that Artem did in the final segment of the show, indicating either that everyone loved Artem or that they did not like Aliona, or both.

This shows again the power of this type of analysis and how it can be used to track a changing public opinion, and give an insight into the reasons why.

Strictly Come Dancing prediction 18th December post-show

Just as in the show, we have collected tweets up until the 8:30pm cut off, and undertaken analysis on the tweets that were made during the show and immediately before and after. This resulted in approximately 10 thousand tweets being made and downloaded, and we have updated our prediction based on this analysis.

The new ordering based on the public's reaction on Twitter to the performances of the celebrities during the show is:

1. Matt Baker
2. Kara Tointon
3. Pamela Stephenson

This ordering has the same winner as previously, and would indicate that Matt has been a consistent performer in terms of voting intention from the tweeting public, if not the general public.  

However, if we include the scores from their dancing partners as well, then we get a completely different picture.  Previously the scores of the dancing partners were not high enough to alter the overall picture, but in this analysis they are sufficiently high to dramatically alter the ordering, as shown below:

1. Kara Tointon and Artem
2. Pamela Stephenson and James
3. Matt Baker and Aliona

This seems to indicate that the winner is particularly open this year.  The extra score for Kara and Artem may reflect the ongoing story relating to their romance?  If so, it would appear to be a good tactic to obtain additional votes.

We should also comment on Pamela's vote - the demographic of her voting public is likely to be older (as she is also older) and it is unlikely that this demographic is well represented on Twitter.  This may mean that her score is being under represented here.  However as shown above, the three contestants are very close in scores based on the tweets made during the show, so the contest appears to be very tight - anyone can win!

Once the results are out we will revisit the contextual analysis of each of the contestants and see if the Twitter data can give us an additional insight into the reasoning behind the public vote.

Strictly Come Dancing prediction 18th December pre-show

We are going to do a couple of blog posts this evening, to see if the public are changing their mind on who they would like to win in the Strictly Come Dancing final. Yesterday we published the results of our analysis of Twitter data, which showed that Matt seemed to be the favourite, with Kara second favourite.

However analysis of the tweets up to about 18:30pm tonight show a slight swing towards Pamela Stephenson, with the ordering based only the tweets from today so far and yesterday as:

1. Matt Baker
2. Pamela Stephenson
3. Kara Tointon

We will update this analysis after the show tonight, to see if there are any late surges for any other contestant and whether either of the girls can displace Matt from the number one position.

Note as always, that this analysis is based on Twitter data only, and therefore the demographic of the tweeting public may not match the demographic of the voting public, therefore these results are indicative only.  This is particularly true for Strictly, as we have not analysed this show before the final before, and so do not how well (or badly!) the twitter scores match against the actual voting trends of the general public.

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BBC Sports Personality of the Year prediction 17th December

As with the X Factor and Strictly Come Dancing analysis, we are analysing twitter data to see if we can predict the winner of the BBC's Sports Personality of the Year.

As noted previously, the X Factor show had a huge number of tweets made about it, with almost a quarter of a million being made on a single Saturday. Strictly has a much lower twitter following, with 25,000 tweets being made in the past week in the run up to the final. The Sports Personality of the Year has even fewer, with approximately 5,000 tweets being made in the last week.

However, we have still been able to undertake our analysis, and the following list has been obtained, in order of likelihood to win:

1. Tony McCoy
2. Mark Cavendish
3. Graeme McDowell
4. Amy Williams
5. Jessica Ennis
6. Tom Daley
7. Lee Westwood
8. Phil Taylor
9. Graeme Swann
10. David Haye

There are some interesting things to note from our analysis.  Jessica Ennis and Amy Williams are very close to each other, and indeed the ordering was the other way around until today's twitter data was included.  It will be interesting to see if the ordering for the girls remains the same over the weekend.  This seems to indicate that there has been a brief surge for Amy, which is likely to be due to the fact that BBC Sport has profiled Amy today.  (Jessica Ennis was profiled over a week ago).

In addition, analysis of the data shows that there was a surge in twitter traffic for Mark Cavendish earlier in the week.  Perhaps this was due to his profile being the first one to be put up on the BBC website (the BBC did them in alphabetical order), but the data shows that his tweets are decreasing day by day.  It looks like the ordering based on tomorrow's data will push Mark further down the list.

We will redo the analysis tomorrow and see if the ordering has changed.

Strictly Come Dancing prediction 17th December

To continue with our theme of analysing reality TV shows, we thought we'd look at the twitter data for Strictly Come Dancing, which has its final show this weekend.

Whereas the X Factor resulted in almost one quarter of a million tweets on a single Saturday night for analysis, Strictly has a much lower tweet count, with almost 25,000 tweets being made over the past week, including last weekend.

Will this affect our analysis? Only time will tell. However clear trends in the data can be seen, and if we repeat the analysis that we undertook for the X Factor, then likely ordering based on twitter data is:

1. Matt Baker
2. Kara Tointon
3. Pamela Stephenson

So this has Matt Baker as the clear favourite.  However if we look at their dancing partners, who are not celebrities, then we see a different ordering:

1. Artem
2. James
3. Aliona

The number of tweets of the celebrities is much greater than that of their dancing partners, so this difference is unlikely to affect the result.  However it does show that if people could vote for the dancing partner only, then it might result in a different winner.

We will repeat the analysis tomorrow to see if there are any late surges by any of the contestants.

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X Factor prediction 12th December

So we have finally come to the end of the marathon that is X Factor! Cher left the show last night, despite a concerted twitter effort by her fans to encourage everyone to vote for her.

An interesting feature of the twitter analysis that we have done is that certain contestants that have been in trouble have had twitter campaigns waged to try to get other people to vote for them! This has meant that analysis like ours which looks at how close in context the contestants are to winning words will be bias towards those people, such as Katie, Wagner and now Cher. A subsequent reaction is then created however by those not wanting these contestants to win, and we find that the same contestants are closest in negative context to the same winning words.

This perhaps gives us a way to try to analyse the data that we have. Which contestants do the public feel most positive about, overall, given the positive and negative context analysis? In other words, if we add the positive context scores for the contestants in context with winning words, and subtract the negative context scores, what ordering do we get? To put it another way, who do the public love the most and hate the least?!

We have analysed the results from last night's show and also the two previous Saturday shows (i.e. we've ignored the tweets made during the week, and concentrated on the highest tweeting activity which is during the show itself on the Saturday). The results are quite interesting, and can be seen in the plot below.



The analysis shows that One Direction started as favourites a couple of weeks ago. In fact according to our analysis, One Direction have been the overwhelming favourites for many weeks now. However it has been noticeable that their support on twitter has begun to drop in comparison to the other contestants.

This then changed to Rebecca as a favourite, until after yesterday's performances we see Matt now as favourite. This perhaps indicates that this year it is quite an open contest! All of the contestants are different from each other and therefore the individual performances count for alot as to how the public may end up voting.

Our final prediction of the year then, based on Twitter data, is that Matt will win, with Rebecca second. One Direction will come in third.

As always it is worth remembering that this analysis is based on twitter data only, and this may not reflect the actual public vote which has a different demographic.

X factor winner - 3 days to go!

With only 3 days to go before the winner of X Factor is decided, we thought it would be useful to do some analysis on what people have been saying on twitter. Again, we should note that the twitter demographic as we know does not match the demographic of the voting public. However we can look to see if there are any trends in the data, or anything else of interest.

One of the things we can do is look at who is context with the word 'win'. This should give us an indication of who is being talked about, and who the twitter public either most want to win, or who they think is most likely to win.

The chart below shows how each contestant has trended in the past 4 days in positive context with the word 'win'. Here we can see a continuation of the high score for Cher from the weekend. However this did not prevent Cher from falling into the bottom two, and suggests that there is an online campaign to try to return Cher as the winner of the X Factor. We saw a similar thing with Wagner, where he scored extremely well (in terms of the number of tweets in context with 'win' and other winning words).



We can also see a similar story for negative context. In this case, we can interpret the graph as meaning which contestant people most do not want to win. Here Cher is again the top scorer, indicating that people either love or hate her. We see Matt and Rebecca tending to do quite well, indicating that the majority of people like them, particularly in comparison to One Direction and Cher.



These graphs only tell part of the story however. Let's look at some negative context analysis for Cher, can we understand why people do not like her? Or conversely why they feel so passionately about her?

In many respects each of the contestants in reaching this stage is a winner in their own right. Each of them have different singing styles and so the winner is more likely to be determined by the demographic of the voting public. Our analysis will now look to focus on brand loyalty in terms of these contestants - what makes them interesting, and what makes them different.

Let's first look at the words in negative context with Cher. As before, the ~ character indicates a 'not' in front of the word, since it was found in negative context with Cher.

05/12/2010 ~cher ~singing ~ballads ~songs ~done ~slow ~would ~isn ~week ~cocky
06/12/2010 ~cher ~lloyd ~mary ~win ~final ~know ~last ~get ~will ~say
07/12/2010 ~cher ~dont ~win ~want ~2010 ~think ~going ~are ~cant ~watch
08/12/2010 ~cher ~lloyd ~ever ~youtube ~heard ~nickiminaj ~amazing ~week ~has ~fierce

We can see the development of the words over each day. At first, we see the focus on her not singing ballads. We then see comments on the fact that people do not want Cher to win.

If we look at the positive context, at first this makes not so much sense.


05/12/2010 cher mary matt rebecca direction voting leak russia leaked x-factor
06/12/2010 cher lloyd malvern gone tickets gig factor 2010 fixing accused
07/12/2010 cher 2010 going can back cherlloyd follow chertowin lloyd love
08/12/2010 cher lloyd cole cheryl malvern cherlloyd performs vote4cher video factor

There is a large focus here on Malvern, which a quick Google search shows is where Cher is from, and is in reference to her returning and performing a small concert. This story is dominating this analysis, so we can look at the next 10 words in context with Cher to see if there is anything else of interest.

05/12/2010 results will tonight was had are final bottom get cheryl
06/12/2010 show bosses metro booted noonelikesyoubecause direction cheryl ferguson nicki really
07/12/2010 still support were retweet gona damn straight amazing has hashtagged
08/12/2010 awkward let dancing cringe fans mobbed followers twitter gains perform

This time we can see some negative comments come through. Evidently the 5th December shows the discussion of her in the bottom two. Subsequent days however has 'noonelikesyoubecause' coming through along with words like: awkward, cringe. So we see a mixed picture for Cher, which reflects the earlier analysis showing that people either love her or hate her.

We will continue this analysis in the coming days, and repeat for the other contestants.

Public reaction to elimination of Mary from X Factor

Last night's show resulted in Cher and Mary in the bottom two. The analysis of the twitter data had Mary in the bottom but Cher had finished top of our analysis - indicating perhaps that her supporters believed that she was in trouble and so mounted a twitter campaign to try to encourage people to vote for her.

We have seen this before - Wagner often finished top of the twitter analysis (particularly in the week that he was knocked out). This demonstrates how campaigns to try to alter public opinion by the supporters of the respective acts have been fought through twitter.

If we look to analyse the data from yesterday's tweets, we can start with Cher, with the negative context analysis can be seen below. This shows the analysis of sentences that contain words such as: never, don't, not, and so on. To understand the analysis, the ~ character indicates that the word 'not' should be placed in front of the word to approximate the meaning of the sentence.

So looking at the figure we can see that the word ~ballad is close in context to Cher, meaning that there has been a great deal of discussion about the fact that Cher did not sing a ballad for her second song. This was picked up in the judges comments but also struck a chord with the twitter public. We can also see some other words such as ~singing, indicating that there were a number of comments about her "not singing", which may be a reference for Cher to her rapping during songs.

The positive context analysis shown below the negative one, does not have a strong positive or negative sentiment, and rather indicates the discussions taking place that focus on the facts of her being in the bottom two, and perhaps comparing Cher to the other artists.

An interesting thing to note in the positive context analysis are the numbers that come through.  You can see them on the left hand side of the word cloud - 0901 and 104.  These numbers are the start and end of the telephone number that you have to use to vote for Cher.  This indicates a last minute campaign by Cher followers to try to influence the final vote and ensure that Cher does not finish in the bottom two.

If we turn now to Mary, we can again first look at the negative context. Note this does not mean the same as negative sentiment, and in Mary's case we see first of all many tweets referring to the fact that she will not be working Tesco again. We also see some other themes coming through such as ~deadlock, indicating that the elimination process did not go to deadlock, and also some negative sentiment words such as ~better, indicating that Mary was not better than Cher or the other contestants. We can also see some interesting words such as ~booed, reflecting the fact that Mary was not booed, but that Cher was when the decision was made.

Finally the positive context analysis shows that the majority of tweets are about Mary 'going home' but also some positive sentiment such as: love, reliable and fabulous. It would not seem that there is great disagreement with the decision from the majority of the public.

This now leads us to wonder who will win? We will continue our analysis during the week to see what trends are forming from our tweet analysis.

X factor result show 6th December 2010

After last night's show there were huge numbers of tweets discussing the performances of the various contestants. We will present here a summary of our contextual analysis of all five contestants, and what this may mean for them in tonights results show.

To start with, we can look at how each contestant fared when analysed in context with winning words - i.e. which contestants, based on the twitter data, look most likely to win the public vote tonight?

Our analysis has the following ranking based solely on positive contextual analysis:

1. Cher
2. Rebecca
3. Matt
4. Mary
5. One direction

This result is particularly interesting.  One Direction have in the past always scored incredibly highly in our analysis, based on their demographic and how well it matches against the twitter demographic.  However, this time they are bottom alongside Mary.  It should be noted that Mary and One Direction scores are almost the same, with a clear gap to Matt and the others above them.

This would suggest that contrary to popular belief, that they may be in trouble in tonight's vote.  Mary also is in trouble looking at the scores, although she has been trending poorly for a number of weeks now.

We can also look at negative context, which means examining which contestants are talked about on Twitter as being who the public do not want to win.  This gives the following ranking:

1. Cher
2. Matt
3. Rebecca
4. One Direction
5. Mary

However, this ranking does not really tell the whole story.  The scores for each contestant are remarkably similar, and so there is not a clear difference between Cher ranked 1 and Mary ranked 5.  This is particularly worrying for Mary, since as we have seen previously she tends not to have as many tweets about her performance.  This would suggest that there is therefore a strong anti-Mary sentiment, when compared to Cher and the other performers.

Also, it would suggest that Cher is very much a contestant that you either love or hate, since she has finished top of both lists.  It is noticeable that Rebecca has moved down the negative context list, meaning that there are comparatively fewer people who dislike her performances when compared to the other contestants.

We can also look at examining the contextual analysis for individual contestants, such as Rebecca.  The figure below shows the analysis of the positive context for Rebecca.  Here we can see lots of positive context coming through, indicating that the majority of tweets were supportive of her performance.

As we can see, there are a number of positive words coming through such as: win, beautiful, amazing, best, grace.  If we look at the negative context for Rebecca then we can identify what issues people had with her performance.  The figure shows the negative context words closest to Rebecca.  The ~ symbol before each word indicates that to understand it, you should imagine a 'not' in front of each word.

Here we can see a number of words coming through, such as: ~dance.  This shows that there are a large number of tweets discussing the fact that Rebecca cannot dance, or does not dance, in her performances. Or perhaps, its because they think she's not suited to singing a dance song. We could create another context cloud for dance to reveal why and give more detail.  We can also see that not everyone likes her voice, as ~voice also comes up close in context.

We can undertake a similar analysis for Mary, where we can see the negative context analysis.

Here we can see that there is discussion about her not returning to Tesco as a checkout girl.  But we can also see that there is rather negative sentiment such as ~good, indicating that her performances were not good, and also ~win, indicating that there are a large number of tweets discussing the fact that they do not think that she can win the X Factor.

It will be extremely interesting to see the results of tonights show, in particular if One Direction do end up in the bottom two.  However, as always, we should take note of the fact that the twitter demographic does not necessarily match that of the voting public.  It is free to make a statement on twitter, but it costs money to actually make a vote.

We will analyse the results show tomorrow, to determine the public's view on who is actually removed from the show, and whether they agree with it.

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Wagner and Katie leave X Factor

So the double elimination resulted in the loss of Katie and Wagner. Our results from yesterday showed that we successfully predicted two of the bottom three (Katie and Mary) but that Wagner's Twitter analysis did not match the public vote. According to our analysis he looked safe and in fact had trended well all week in context with winning words.

So what went on? This is an interesting question, and gives an insight into the dangers of taking at face value what people post on the internet. If we examine the words that are in context with Wagner based on Saturday's data (i.e. before the elimination), then a picture emerges that concurs with our initial analysis. This can be seen in the figure below, where the closer the word is to the centre, then the closer that word is in context to Wagner.

The analysis shows how there is a campaign on twitter to either encourage people to vote for Wagner, or an expression of the hope that they would like Wagner to win. This does not necessarily mean that people have actually voted, and here we can see a disconnect between what is being said (i.e. Vote for Wagner!) and what is actually happening (i.e. people who tweeted did not vote for Wagner to the same scale). This is important to note from a brand management perspective - just because online content seems to be saying one thing, it does not mean that the users of that brand will actually do it.

When we look at the other words surrounding Wagner in context, we see a different point of view emerging. Words such as: annoying, ruin, going home, shite, too long, shit, kill. These words are all of a negative context and relate to an emerging theme which would show that people have had enough of the joke of keeping Wagner in the competition. In fact there are very few positive words coming through at all.

This highlights again the need to ensure that data sources are identified that cover all demographics so that the data analysis is not too biased on way or another.

If we turn to Katie, we can again analyse what words are in context with her based on Saturday's data (i.e. before the elimination). Again, the closer the word is to the centre, then the closer that word is in context with Katie.

This time we see a different picture. We do not see words like win or vote come up close in context to Katie. Instead we see a number of different contestants such as Mary, Wagner and Cher. This is related to discussions that were being had by the general public on who would be likely to be eliminated, as we see by the words Sunday and eviction also coming through. We also see Olly Murs coming through strongly in context, relating to the interview that he gave where he talked about Katie. We also see a large number of negative sentiment words, such as: murdered and killing, which relate to her performance of Sex on Fire. Interestingly we do not see much comment on her second song 'Everybody hurts', which does not appear to come through as closely in context.

However it can be seen here that there is largely a negative reaction to her performances and so it is clear why she was voted out.

Finally we can look at Mary. Again the analysis is based on Saturday's data (i.e. before the elimination) and the closer the word is to the centre, the closer that word is in context with Mary.

Here we see very little comment on her singing performance. However we see a number of words coming through in context that may initially surprise us as being in context with Mary - bearing in mind she is over 50! We see the word thong very close in context along with other words: lucky, Tesco, value, smells. Although this would at first glance appear to be some form of mistake, it is related to the story that Mary has taken to wearing her 'lucky Tesco value thong' when she goes on stage, but that since she has been wearing it for every show, it now 'smells'. Not a very nice mental image, whichever way you look at it!

This highlights once again how the analysis of the data in a contextual manner brings out unexpected and new information about a brand or person, and gives an insight into the public's view on any particular topic.

We will continue our analysis in the run up to the semi finals next week, where we will focus on the positive sentiment to identify who is most likely to be safe from the dreaded elimination. Will Twitter match the actual votes made? Now that Wagner has been removed from the competition, will we see more of a convergence of twitter trends and actual voting trends? In other words, will brand loyalty as evidenced on twitter convert to real votes in the show? As always, we will complement our scoring analysis with contextual analysis to reveal the reasons behind the voting decisions that the public have made - and highlight again how contextual analysis is the only method that fully explains the diverse views of the public and what they are thinking.

X factor analysis 28th November

We are rapidly losing contestants now, and so we can look at examining the scores for them all together. The figure below looks at the analysis for each contestant based on tweets over the past week. Each day is analysed separately, and is shown in terms of how close in context each contestant is to words such as 'win'.

The graph shows the rank of each contestant for each day. So Wagner actually scores the best for almost every day (i.e. he ranks as number 1), whereas Mary and Rebecca are scoring poorly with one of them normally 7th (i.e. worst rank) with the other 6th.



As we can see in the figure, there is a huge amount of support for Wagner, which would suggest that he will be safe again this week. This may surprise some people but shows the depth of feeling and perhaps the unique British sense of humour that is looking to produce an unusual winner for this most popular show. Certainly, there has been a campaign online to keep Wagner in the competition, and judging by the analysis here that is certainly being successful. Whether the twitter public want to wind up Simon Cowell or whether they genuinely enjoy Wagner's performances, the end result appears to be that he will again be safe this week, if the general public reflects the view on twitter.

This analysis may not however reflect the views of the voting public, which as has been shown previously has a different demographic to that one twitter. However, it will be interesting to see if his support continues to the same level should he indeed remain in the show when we start to approach the final.

In terms of who is in trouble, the bottom two according to the analysis on twitter is again Mary and Rebecca. We can see that these two were the bottom two last week as well, which is a worry for both of them since we can see that their support is therefore consistent. Last week however Cher and Paije were in the actual bottom two of the show, suggesting that Mary and Rebecca's support if under-represented on twitter. If we look at Cher's support last week and compare to this week, we see that she is doing better this week than last, suggesting that she may be ok and avoid the bottom two.

Katie as always is towards the bottom of the analysis, and we see an opposite trend for her. Whereas last week she performed better on our twitter analysis (finishing above Cher), after yesterday's show she has dropped into our bottom three, suggesting that she will be in trouble as she has a vocal support on twitter that is over-represented when compared to the general public.

This evenings show is a double elimination, so TWO people will go. Going from our analysis then, two of Mary, Rebecca and Katie look likely to go, however as we know this does not always reflect the views of the voting public. It will be interesting to see how the votes pan out! Will Wagner remain in the show to sing another week?

Cher contextual analysis

So was it surprising that Cher reached the bottom two yesterday? Certainly, she was not bottom two according to the twitter comments but as we know, this is not necessarily reflective of the voting public.

As we have seen from previous weeks, it is clear that some contestants garner a large support despite not being tweeted about (see Paije until this week and also Mary), whereas others have a large body of apparent support on twitter but find themselves in the bottom two (see Katie before this week for example of this).

Cher also has a large body of support on twitter, but what does her support look like when we examine what is being said about her in context?

If we analyse the data from Sunday up until the show (i.e. before 8pm) then we see some surprising results. The picture below shows words that are in context with Cher. The closer the word is to the centre, the closer in context to Cher that word is.

We see a large number of negative words such as: bottom, annoying, rubbish, crap, tank. There is some good sentiment type words too but there are fewer of them, such as: good, babe.

This comes from people tweeting on the Sunday, so perhaps reflects more the view of the general public and not her ardent fans who will have enjoyed whatever she did.

We can do the same analysis for Rebecca, who based on Saturday's tweets we had in the bottom two. This analysis can be seen in the figure below, where as before the closer the word is to the centre, then the closer in context that word is to Rebecca.

Here we can see that although there is some negative sentiment such as: murdering, hate, crap. However we see alot of positive sentiment also coming through such as: vote, favorite, want, win, supporting, love. We also see a fair amount of context coming through based on what Rebecca was wearing, such as: wearing, vintage, necklace.

This shows much more positive analysis than Cher over the same period (Sunday up until the 8pm results show). We will extend this analysis over the week to look at how changing context on the Sunday reflects the public's opinion on the voting result and subsequent loss of Paije from the competition.

Bottom 2 prediction 21st November

Very quick post today, simply to show predicted bottom two based on our analysis of tweets from Saturday.

Our ranking is based on how closely in context the contestants are to positive sentiment words such as 'win', or 'winner'.

The bottom four according to our analysis is (with 1st at the worst, and 4th as the best of the four):

1. Rebecca
2. Mary
3. Paije
4. Katie

This means our bottom two this week are: Rebecca and Mary, rather surprisingly. However as we saw last week analysis of trend may show more, which we will focus on tomorrow.

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X factor analysis 15th November

So Aiden and Katie reached the bottom two in last night's show, with Aiden receiving the fewest number of votes and was evicted from the competition.  It is interesting to note that the bottom 4 as we predicted yesterday, were the last 4 contestants in the show as they were made safe.  So although Dermot states that the contestants are told that they are safe in no particular order, it would make sense to increase the tension to not have contestants that are obviously safe in the 'drop zone'.

We have had a similar result before - Paije does not trend well in twitter, and there are very few comments made about him.  This is interesting in itself, since he has not yet been voted into the bottom 2, so there is therefore a large demographic voting for him that does not have a presence on twitter.  This highlights the need to ensure that data sources that cover all demographics are identified for any marketing or PR campaign.

The same largely goes for Mary too, although it is noticeable that her support on Twitter has diminished over the past few weeks, suggesting that she may be in trouble in the coming shows unless she is able to either connect better with the audience or produce better singing performances to keep her in the competition.

It is also interesting to examine the pattern of the results over a number of days.  We said yesterday that Mary and Paije were in the bottom two based on Saturday's results only, however if we average over a number of days, then we end up with the following result, that can be seen in the chart below:



The analysis shows that if we look at the average score over the past 10 days, we see that Aiden has in fact been overall in the worst position.  We see that Mary and Paije have similar levels of support, and that Katie out of the four contestants analysed here has a core following on Twitter that means she consistently does better.

This analysis therefore perhaps puts yesterdays result into perspective. Katie has a core support which will vote for her regardless of her performance.  From previous experience on X Factor we can see that this is necessary, and explains why she continues in the competition, despite there being so much negative press about her.

Aiden on the other hand, has fluctuated dramatically over the past 10 days, with his support not as consistent. For a contestant in this position, he is always one poor performance away from being voted out.

The analysis also indicates that Paije and Mary may also struggle in the coming weeks, although again good performances may buffer them from dropping into the bottom two.

X factor prediction 14th November 2010

Quick post today - simply showing our analysis of the contestants in trouble according to the analysis of twitter data.

The analysis can be seen below, and appears to show that Paije and Mary are in the bottom two.  However, previous results have shown that Paije in particular does not result in a large number of tweets and so the results may be bias against him in particular.

The results for the other contestants showed a clear gap between these four and the other contestants.  However as noted previously, these results may not reflect the vote from the general public if the demographic on twitter is not similar to the voting public.

This week does appear to not be as clear cut as previous weeks, although as noted above the four shown below do appear to be cut off in terms of popularity from the other contestants.

We will continue this analysis tomorrow after the publication of the bottom two and who ends being evicted from the show.

X factor results

The results show last night had Trayc and Katie in the bottom two, which tallied with our predictions of yesterday. As a follow up we thought we'd look at what was said in context with Katie to see if there are any clues as to why she is not garnering the votes and what the public are saying about her.

The image below shows the words closest in context to Katie. The closer the word is to the centre of the cirle, then the closer in context the word is.

The analysis above shows the words that are in context with Katie. As can be seen, there are a large number of negative comments, such as 'awful', 'cringing', 'worst', 'pained', 'poorly', 'rubbish', and 'hate'. It is also noticeable that Katie comes in close context to Wagner, which is not necessarily a positive connection since although Wagner has not yet been in the bottom 2, he is seen largely as a figure of fun and not a serious contestant by the majority.

It is not all bad news however, there are some positive words such as 'fit', 'girlfriend' and 'best'. So there is hope for Katie although it would seem from this analysis that the majority of the public do not connect with her performances in the same way as with other contestants.

X factor analysis 7th November

So ahead of the publication of the results tonight we thought we would again show analysis of the tweets that have been published around the Xfactor this week.


This time around, we will try to take into account the bias that seemed to show through with twitter from last week. Although we consistently had Belle Amie in the bottom two we also had Paije performing poorly and yet he was not in the bottom two. In addition, Katie was voted into the bottom two and we had thought she would be safe based on online comments and was only saved as Belle Amie had fewer votes than her.


The analysis of the online content for this week can be seen in the graph below (with the dates shown on the bottom, with yesterday's tweets coming through for the 6th Nov), which shows the ranking for each contestant against each other (i.e. 1st means that they scored the highest for that particular day):







We have included Cher and Mary this week as both of their scores were of a similar level to Aiden.


The analysis is interesting for a number of reasons.  Firstly we can see a definite trend with respect to Paije, and for the first time we see a large number of positive comments about him coming through on twitter.  The comments for Cher started off as the highest ranking but dropped towards the end of the week and on Saturday in particular, which is a worry for her.


The same goes for Aiden, who has a worrying trend for him in that he is consistently near the bottom end of the scores.  Perhaps people are struggling to relate to his intense singing style?  Mary should also perhaps be concerned since a good trend in tweets during the week is reversed on the Saturday, perhaps reflecting her poor performance on the night.

Trayc yet again does not score highly, and would appear from this analysis at least to be at risk.  However this was also the case last week, and the twitter demographic does not necessarily match the voting public.

Yet again perhaps to some people's surprise Wagner does not appear on this list as his scores were far in excess of the ones listed here.

We will pick up this analysis tomorrow, after the bottom two and eviction of one of the contestants has been completed.

Belle Amie voted out

So in the evening voting show Belle Amie were shown to have received the lowest number of votes and left the competition. Does this tally with our predictions of yesterday?

The bottom 6 according to our analysis on twitter chatter contained both Katie and Belle Amie, and so this aspect of the analysis was correct. However we had Paije as the worst performer along with Belle Amie and Trayc. Xfactor does not release the exact ordering of the contestants, so we don't know who finished 3rd from bottom. But why does our analysis show a different bottom two than were voted for by the public?

We can see the reasons why when we examine the rankings that we posted yesterday in a little more detail.

The chart below shows normalised positive sentiment analysis for the contestants every day based on twitter comments. We haven't looked at negative sentiment since the Xfactor requires you to vote FOR your favourite contestant - therefore the more positive that people are being about a particular contestant, the more likely that contestant will do well in the public vote. The analysis is normalised so that each day can be more easily compared against each other - there is evidently much more chatter on the Saturday and Sundays about the Xfactor when the program actually airs.

We can see in the figure that Aiden tends to have the most positive comments, but we also see Katie doing well. Looking at the positive sentiment, it is clear that Paije for example consistently barely scores at all - this is due to the fact that there are very few comments being made about Paije at all on twitter. The same is true for Trayc and Belle Amie. On a number of days there is no chatter about Trayc at all, and although there is consistent chatter about Belle Amie it is small in comparison to Aiden, and even Katie.

This highlights a couple of obvious conclusions that all digital marketeers should be very aware of! Firstly, the demographic of twitter does not match that of the voting public. It may be possible to identify trends in the populace - for example the high scoring of Wagner throughout the week indicating that there is a large body of support for him. However particularly at this early stage where the vote is split it can be difficult to predict with any accuracy how the general public will vote from the twitter data alone.

This highlights the second conclusion which means that in order to improve our predictive capability, alternative data sources should be identified that more closely match the voting intentions of the general populace. It is unlikely that a single data source will cover this wide demographic and therefore a combination of data sources should be used.

X factor analysis

Well we're moving into autumn and winter which can only mean one thing - its time for X factor!  Love it or loath it, you cannot ignore it, and we thought we'd look at applying some of our analysis techniques to the twitter chatter on the X factor finalists.

The analysis below shows sentiment analysis on twitter chatter on the finalists over the past week (including both Saturdays, the 30th and 23rd October), and we have focussed on those that have scored the worst. In the chart below, the finalists are ranked against each other, so Aiden has more often than not been ranked 1st and therefore done the best out of those not doing so well.

The analysis shows that Paige and Belle Amie consistently do badly, with Trayc also doing poorly. However Katie and Aiden in this group at least are performing well and would seem to be safe for the next few weeks.

Twitter Analysis: BP 210,000 tweets analysed, emerging stories and trends over a week

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Twitter Analysis

Our twitter analysis packages start at only £300 per month, for more details on the packages please view our website products page.  The keywords used here are: BP, Obama, Cost, Ocean. The cost of this analysis below would be only £400 a month, so only £100 for the weekly analysis as demonstrated in this BP example.  Incredible value for reputation management, and market research and other PR and marketing listening and measurement activities. 

Contextual trend analysis of BP


We have analysed twitter responses about BP in context over a week period, 06.06.10 to 12.06.10. 

Source: 210,000 twitter comments.


BP & Obama
In twitter comments mentioning BP, Obama was mentioned most regularly in the following context:
06.06.10 – spoken, hasn’t, directly, captured, being.
07.06.10 – spoken, hasn’t, president, doesn’t, administration, boot, neck, American.
08.06.10 – lashes, media, chief, afp, barack, fire, crisis.
09.06.10 – downplays, number, attack, enemy, public, osama, guardian, fears.
10.06.10 Day 3 – chairman, week, meet, invites, administration, update, Washington, allen.
11.06.10 – Britain, bullying, stop, demonising, says, cantor, eric, news.
12.06.10 – promise, ready, president, suspend, executives, retain, cash, mee (Massey Energy Co.)
The individual words build a story of Obama's reaction to the BP disaster.



BP & Cost
In twitter comments mentioning BP, cost was mentioned most regularly in the following tweet context:07.06.10 – billion, far, captures, reach, cleanup, same, current.
08.06.10 – florida, report, 195, jobs, real, flow, billions.
09.06.10 – pressure, political, default, company, soar, swaps, back, then.
10.06.10 – jobs, billion, 195, dollars, workers, fla, dollars, workers, 189.
11.06.10 - cost, real, estate, billion, coud, values, property, homeowners, clean.
12.06.10 - began, shares, almost, value, halved, people, mcartney, derek.

Over time, different costs emerge giving a wider picture of the far reaching negative effects of the BP disaster.

BP & Ocean
In twitter comments mentioning BP, ocean was mentioned most regularly in tweets containing these words:


06.06.10 – wants, fake, twitter, mocking, shut, ruining, account, nice.
07.06.10 – new, black, tumblr, blue, usual, replacing, fire, rig.
08.06.10 – shut, account, ruining, response, thats, company, ryanvaughan, acct.
09.06.10 – wants, fake, mocking, account, shut, ruining, company, want.
10.06.10 – million, campaign, spending, cleaning, agree, boycotting, too, aral.
11.06.10 – let,destroy, products, list, cannot, global, break, repercussions.
12.06.10 – let, destroy, list, won, products, destroying, cannot, break.

It appears in a backlash to BPs damage to the Ocean, there is an ongoing attempt to ruin BPs image with a infamous fake BP twitter account and list of products to boycott.

BP in context

The most common words surrounding BP continually over the week period are: spill, oil, news, Mexico, leak, gulf, disaster. The other top words surrounding BP in tweets are:06.06.10 – well, broken, captures, cap, containment, capturing, far, working, increasing, Won, chief, quit, florida, protest, dozens, reuters, news, yahoo.

07.06.10 – news, google, buys, search, words, real, keep, people, yahoo, coast, clean, guard, take, years, drilling, May, impacting, 120.

08.06.10 – google, buys, top, result, has, sponsored, links, purchased, appear, mashable, search, yahoo, terms, words, news, people, real, keep, engine, phrases, folks.

09.06.10 – leak, plans, clean, stop, hours, solid, contingency, complete, mexico, are, govt, doing, truth, leaders, speaks, citizen, spill—the, sharing, music, owes, damaging.

10.06.10 – Costner, day, water, million, Kevin, machines, gallons, sells, recycle, patent, technology, ignored, been, abt, remarks, making, snarky, barrels, new, gulf.

11.06.10 – are, doing, govt, leaders, truth, speaks, citizen, spill—the, could, video, cats, Nsfw, /via, kim, deals, coffee, know, needs, plug.

12.06.10 – obama, barack, nick, clegg, doing, are, truth, speaks, govt, citizen, spill—the, leaders, failures, fighting, can, gusher, made, worse, woes.The above words show that new stories and trends in context with BP are emerging daily.


We can analyse blogs, forums, and twitter comments seperately or combine all comments to demonstrate a wider picture on a topic across all these digital sources.

Please get in touch with jayne@brand-aura.com if you'd like further details.
If you want social media analysis carried out similar to the election trends analysis, whether political or another topic. Please view our affordable prices for Twitter and Social Media packages on http://www.brand-aura.com/products.html

Please get in touch with jayne@brand-aura.com.

Brand-Aura launches http://www.brand-aura.com/ and is featured in leading Web Magazine Semantic Web

Discover more about Blueflow Technology http://www.blue-flow.com/The Twitter search application can only go far back as approximately two weeks and due to the twitter API it may not contain all tweets made on this subject.