For the OP part 2 we could carefully observe the political conversations on our social media. Since I have quite some friends on my FB who are in politics or political engaged, I expected to find some political conversations.



On my FB wall I looked back for a few days, and indeed there were some political conversations. But all of them were really short, and not really good long discussions. I actually also think FB is not really the right platform for this.

But there was some discussion about the increase of asylum seekers in the North of the Netherlands, and the political response to that. Furthermore one about graffiti of Putin’s face across Belgrade. And also an discussion about the biased reporting of the Dutch media regarding crime in the Netherlands. In addition, there were a lot articles shared about the situation in Iraq and Syria, and some opinions about that.



Black Pete

Anyway, last but not least, a lot political related conversations on my FB discussed only one thing: Zwarte Piet, Zwarte Piet! Personally I want to stay away from the discussion as far as possible since most people, both pro- and contra-, are coming up with the most awful and ridiculous arguments. But it’s a hot topic all over social media. Also if we look to the top 5 trending topics on twitter this becomes clear: Rotterdam (clearly because it’s awesome) , Zwarte Piet, Waarom (=why), Sinterklaas, The Netherlands.

If we analyse some tweets for Zwarte Piet with Topsy, the sentiment is surprisingly equal, just a bit more negative tweets.

With Topsy we can also see that the amount of Tweets about Zwarte Piet had some big peaks since 8 october. This can be explaned because of the fact that on that day Albert Heijn announced to decrease the amounts of black petes in their supermarkets.

With Sentiment Viz we can take a closer look to the sentiment of Zwarte piet on Twitter:

In the visualization underneath we can see that most tweets are the green dots. They have a quit positive, pleasant content. And don’t have really clear emotions in them. It’s globally divided like this, with on each side different emotions. (See for yourself: http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/ )

Active

Unpleasant Pleasant

Subdued

This is supported by the Heat Map in which Grid cells with more than an average number of tweets are Red and grid cells with less than average are blue. There most red Grid cell are the tweets on the ‘pleasant’ side, but very close to the middle, so pretty neutral.

If we take a look at the Tag cloud, we can see which tags are mostly used in tweets in the different categories. For instance we see ‘white’, discussion on the more pleasant side. And Stop, Strike, Wrong, Shit at the more unpleasant site. Nevertheless, also at the pleasant side we can see Shit, Banned etc. but the content of the tweets can still be pleasant and positive.