The Last Mile in the Belgian Elections (VII)

The Flemish Parliament’s Predictions

Scope management is important if you are on a tight budget and your sole objective is to prove that social media analytics is a journey into the future. That is why we concentrated on Flanders, the northern part of Belgium. (Yet, the outcome of the elections for the Flemish parliament will determine the events on Belgian level: if the N-VA wins significantly, they can impose some of their radical methods to get Belgium out of the economic slump which is not very appreciated in the French speaking south.)  In commercial terms, this last week of analytics would have cost the client 5.7 man-days of work. Compare this to the cost of an opinion poll and there is a valid add on available for opinion polls as the Twitter analytics can be done a continuous basis. A poll is a photograph of the situation while social media analytics show the movie.

 A poll is a photograph of the situation while social media analytics show the movie.

From Share-of-Voice to Predictions

It’s been a busy week. Interpreting tweets is not a simple task as we illustrated in the previous blog posts. And today, the challenge gets even bigger. To predict the election outcome in the northern, Dutch speaking part of Belgium on the basis of sentiment analysis related to topics is like base-jumping knowing that not one, but six guys have packed your parachute. These six guys are totally biased. Here are their names, in alphabetical order, in case you might think I am biased:

Dutch name	Name used in this blog post
CD&V (Christen Democratisch en Vlaams)	Christian democrats
Groen	Green (the ecologist party)
N-VA (Nieuw-Vlaamse Alliantie)	Flemish nationalists
O-VLD (Open Vlaamse   Liberalen en Democraten)	Liberal democrats
SP-A (Socialistische Partij Anders)	Social democrats
VB (Vlaams Belang)	Nationalist & Anti-Islam party

Table 1 Translation of the original Dutch party names

From the opinion polls, the consensus is that the Flemish nationalists can obtain a result over 30 % but the latest poll showed a downward trend breach, the Nationalist Anti-Islam party will lose further and become smaller than the Green party. In our analysis we didn’t include the extreme left wing party PVDA for the simple reason that they were almost non-existent on Twitter and the confusion with the Dutch social democrats created a tedious filtering job which is fine if you get a budget for this. But since this was not the case, we skipped them as well as any other exotic outsider. Together with the blanc and invalid votes they may account for an important percentage which will show itself at the end of math exercises. But the objective of this blog post is to examine the possibilities of approximating the market shares with the share of voice on Twitter, detect the mechanics of possible anomalies and report on the user experience as we explained at the outset of this Last Mile series of posts.

If we take the rough data of the share-of-voice on over 43.000 tweets we see some remarkable deviations from the consensus.

Party	Share of voice on Twitter
Christian democrats	21,3 %
Green (the ecologist party)	8,8 %
Flemish nationalists	27,9 %
Liberal democrats	13,6 %
Social democrats	12,8 %
Nationalist & Anti-Islam party	11,3 %
Void, blanc, mini parties	4,3 %

Table 2. Percentage share of voice on Twitter per Flemish party

It is common practice nowadays to combine the results of multiple models instead of using just one. Not only in statistics is this better, Nobel prize winner Kahneman has shown this clearly in his work. In this case we combine this model with other independent models to come to a final one.

In this case we use the opinion polls to derive the covariance matrix.

Table 3. The covariance matrix with the shifts in market shares 

This allows us to see things such as, if one party’s share grows, at which party’s expense is it? In the case of the Flemish nationalists it does so at the cost of the Liberal democrats and the Nationalist and Anti-Islam party but it wins less followers from the Christian and the social democrats. The behaviour of Green and the Nationalist and Anti-Islam party during the opinion polls was very volatile, which explains for a part the spurious correlations with other parties.

Graph 1 Overview of all opinion poll results: the evolution of the market shares in different opinion polls over time.

Comparing the different opinion polls, from different research organisations, on different samples is simply not possible. But if you combine all numbers in a mathematical model you can smooth a large part of these differences and create a central tendency.

To combine the different models, we use a derivation of the Black-Litterman model used in finance. We are violating some assumptions such as general market equilibrium which we replace by a total different concept as opinion polls. However the elegance of this approach allows us to take into account opinions, confidence in this opinion and complex interdepencies between the parties. The mathematical gain is worth the sacrifice of the theoretical underpinning.

This is based on a variant of the Black-Litterman model  μ=Π+τΣt(Ω+τPΣPt)(p−PΠ)

And the Final Results Are…

Party	Central Tendency of all opinion polls	Data2Action’s Prediction
Christian democrats	18 %	18,7 %
Green (the ecologist party)	8,7 %	8,8 %
Flemish nationalists	31 %	30,3 %
Liberal democrats	14 %	13,7 %
Social democrats	13,3 %	13,3 %
Nationalist & Anti-Islam party	9,4 %	9,8 %
Other (blanc, mini parties,…)	5,6 %	5,4 %
Total	100 %	100 %

Table 4. Prediction of the results of the votes for the Flemish Parliament 

Now let’s cross our fingers and hope we produced some relevant results.

In the Epilogue, next week, we will evaluate the entire process. Stay tuned! Update: navigate to the evaluation.

The Last Mile in the Belgian Elections (IV)

How Topic Detection Leads to Share-of-voice Analysis

It was a full day of events on Twitter. Time to make an inventory of the principal topics and the buzz created on the social network in the Dutch speaking community in the north of Belgium.

First, the figures: 10.605 tweets were analysed of which 5.754 were referring to an external link (i.e. a news site or another external web site like a blog, a photo album etc…)

As the Flemish nationalist party leader Mr. Dewever from N-VA (the New Flemish Alliance in English) launched his appeal to the French speaking community today, we focused on the tweets about, to and from this party.

A mere 282 tweets were deemed relevant for topic analysis. And here’s the first striking number: of these 282 tweets only 16 contained a reactive response. 

Tweets that provoked a reactive response are almost nonexistent

About 49 topics were grouping several media sources and publications of all sorts. We will discuss three to illustrate how the relationship between topic, retweets, klout score and added content makes some tweets more relevant than others. These are the three topics:

• Dewever addresses the French speaking community via Twitter
• Christian Democrat De Clerck falsely accuses N-VA of using fascist symbols in an advertisement
• You Tube movie from N-VA is ridiculed by the broad community 

Dewever addresses the French speaking community via Twitter

This topic is divided in a moderately positive headline and two neutral ones. The positive: Bart Dewever to the French Speaking Community: “Give N-VA a Chance”

This headline generates a total klout score of 188 where the Flemish tv station VRT takes the biggest chunk with 158 klout score.

This neutral headline generates only 98 klout score: “Dewever puts the struggle between N-VA and the French speaking socialist party at the centre of the discussion”

The other neutral headline “N-VA President Bart Dewever addresses the French speaking community directly” delivers a higher score: 140 klout score partly because one of N-VA’s members of Parliament promoted the link to the news medium.

All in all with 426 total klout score, this topic does not cause great ripples, especially not if you compare this to a mere anecdote, which is the second topic.

Christian Democrat De Clerck falsely accuses N-VA of using fascist symbols in an advertisement

On the left, the swastika hoax, commented by the christian democrat and in the right the original ad showing a labyrinth

Felix De Clerck, son of the former Christian democrat minister of Justice Stefaan De Clerck, reacted to a hoax and was chastised for doing this. With a klout score of 967 this has caused a bigger stir although the political relevance is a lot smaller than Dewever’s speech… Emotions can play a role even in simple and neutral retweets.

You Tube movie from N-VA is ridiculed by the broad community

Another day’s high was reached with an amateuristic and unprofessional YouTube movie which showed a parody on a famous Flemish detective series to highlight the major issues of the campaign. This product from the candidates in West-Flanders, including the Flemish minister of Interior Affairs, Geert Bourgeois generated a total klout score of 778 tweets and retweets with negative or sarcastic comments.

Yet an adjacent topic about a cameraman from Bruges who is surprised by minister Bourgeois’ enthusiasm generates a 123 moderately positive klout score.

Three topics out of 49 generate 20.6 % of total klout scores!

This illustrates perfectly how the Twitter community selects and reinforces topics that carry an emotional value: the YouTube movie and the hoax from De Clerck generated a share of voice of no less than almost 17% of the tweets.

Forgive me for reducing the scope to Flanders, the political scope to just one party and the tweets to only three because this blog has not the intention of presenting the full enchilada. I hope we have demonstrated with today’s contribution that topics and the way they are perceived and handled can vary greatly in impact and cannot be entirely reduced to numbers. In other words, the human interpreter will deliver added value for quite a long time.