People are very divided when it comes to certain issues such as politics, abortion, gun-control and same-sex marriage. When two groups have conflicting and opposing viewpoints with very few people holding a neutral viewpoint, it is known as ‘polarization’ in social science. With the rise of social media and technology use, more people are able to voice their opinions online which allows us to analyze which topics cause polarization.
Why is it important to study polarization? According to the paper (cited below), polarization causes segregation and political conflict. I chose this paper since it tries to study polarization using community detection algorithm which is a topic we learned in class. The paper also shows how their measure of polarization is related to prior work on this matter which used a measure known as modularity. Modularity works by quantifying homophily and antagonism. Homophily was also studied in this course.
This paper tries to derive a novel approach to analyze polarization compared to prior work on this matter. It is important to understand the concept of antagonism in graphs prior to understanding this paper’s methodology. Antagonism tries to quantify nodes avoiding to connect with nodes of another community. This provides the intuition for the following idea used in this paper.
In this paper, firstly, the communities are identified using some community detection algorithm. Then the boundary nodes are identified within each of the two communities. This paper defines the boundary node as having one of two properties.
- A node v ∈ Gi has at least one edge connecting to community Gj ;
- 2. A node v ∈ Gi has at least one edge connecting to a member of Gi which is not connected to Gj
Finally, they measure the connectedness of these boundary nodes. The less connections between these boundary nodes implies higher polarization.
This paper observes a real world network of opposing views on gun control following the tragic shootings in Sandy Hook Elementary School in Connecticut by analyzing tweets and retweets on Twitter.
Here, the polarity constant being positive implies there is polarization and negative means very less polarization or surprisingly more cross-connections between boundary nodes.
In short, we can see how community detection algorithm and other concepts from this course can be used to analyze polarization. We also know why it is important to minimize polarization. With continual knowledge and improved algorithms to detect polarization, we can hope to find solutions to this problem.
Reference List
- Guerra, P. C., Meira Jr, W., Cardie, C., & Kleinberg, R. (2013, June). A measure of polarization on social media networks based on community boundaries. In Seventh International AAAI Conference on Weblogs and Social Media.