Author: Joshua Aaron Concon

I’m a big fan of the concept of the Balance Theory concept that we learnt earlier in this class. The idea that given a complete graph K_3, that only the following signed edges are considered balanced: +++, -+-. In my previous article, we explored how Balance Theory can be applied to relationships (in a TV Show nonetheless), and I ended off talking about how it would be interesting in to see how this can be applied to politics.

I think politics can be seen similarly to drama and relationships in this sense. Countries that cooperate with each other or countries that are considered to be “stable” can be seen as balanced edges, while conflict, either between countries or within a country, can be seem as unbalanced edges. Although it may be a stretch, I also think a political deadlock such as the UK over the Brexit process can be similarly analyzed just as Friends was analyzed. The process is not moving forward smoothly due to conflicting incentives, resulting in party infighting, infighting within British parliament, and friction between both the British People and their Representatives in Parliament.

So when looking for an article to write about for this blog post, I was skimming the Wikipedia page for Balance Theory where I noticed that it gave an example of the Theory being used through Celebrity Endorsements. This made sense, in that, if a viewer were to see a celebrity that they liked endorsing a product, they would be moved to also take interest in that product. This also meant that if the viewer did not like the celebrity endorsing the product, they would also develop disdain for the product. This sort of logic could be the explanation for the partisanship we see today, in modern politics.

I also ran into this journal: https://journals.sagepub.com/doi/pdf/10.1177/1532673X8100900303. It’s about the analysis of applying balance theory between voters, political issues and Gerald Ford. After the data has been collected, it is shown that the predictions that Balance Theory would suggest are probable.

Recently in one of my Social and Information Network lectures, we discussed the idea of Structural Balance. This was essentially the idea that if a network had signed edges (positive or negative), then we could classify the network as stable depending on the patterns every triangle that was made with the signed edges made.

Since the usual example that was used for our networks was social networks, it was easy to see this representation as a social network of people, where the signed edges either meant two people liked or hated each other. In that case, a triangle with only one positive edge would represent two friends who both hate the person, and a triangle with only one negative edge could represent a love triangle, where two people are competing against each other for the love of the other person.

When I pointed the love triangle case out in class, I got a few laughs. The professor ended up stating that the love triangle case is also not stable. Although it is strange and possibly funny, I do find it interesting that we could use graph theory, and other course content that we learnt in class, to analyze the different structures that may come up when analyzing relationships in a drama for example. Maybe it’s possible to analyze a drama episode by episode, mapping out the relationship network to predict interpersonal conflict between the different characters depending on the stability of the triangles. Since love triangles are a common plot device, maybe you could even use this to predict what happens next episode, if the negative and positive edges indicate the intensity of the relationship between two nodes.

I then decided to search around for articles around this concept and found this one. It’s not super recent (2015), but here they trained a neural network on the TV show “Friends”, (the scenes and the subtitles). From that, they were able to generate this affinity chart.

Admittedly, I’m not 100% sure of how they generate this chart, but it’s neat that tracking this is even possible. I wonder if this can also be used to predict how countries interact with each other. I did find another paper that measures international relations by the use of social media, but given the use of bots on social media, I decided not to look further into it.