# Immigration as a Divisive Topic: Clusters and Content Diffusion in the Italian Twitter Debate
## 3 Methods and Results
### How Do They Interact? Analysis of the RT Network
#### 3.2.1 Community Structure and Analysis of Content: Polarisation of the Debate  
In order to capture the polarisation of the debate, we investigate the community structure of the network. Empirical evidence tells us that retweet (and quotes, to a slightly lower extent) can be seen as endorsement [11], especially when the conversation happens in restricted communities rather than across a non-cohesive network [34]. Therefore, we check the most internally cohesive sub-groups of the RT network to find those nodes who are most likely to share the same stance towards the topic of migration. We use the well-known Louvain method for community detection, an agglomerative algorithm that merges together groups of nodes to maximise modularity [35]. In our case, the best partition has a modularity of 0.48. It is interesting to look at the composition of the communities. As we can see in Figure 4, there is a high number of micro-communities composed of only two or three nodes. We focus on those groups who present at least 1000 nodes and with more than 100 RTs that could be traced back to their members. This results in the five communities described succinctly in Table 4. Moreover, by looking at the top 10 most highest in-degree nodes in each community, we can provide a conceptual annotation of the various groups. Their in-degree distribution can be seen in Figure 5.  
Figure 4: Size of the communities. There is a great number of micro-communities made of only two or three nodes. The first 5 communities group together 87% of the nodes, leaving out only 29,969 nodes. The biggest community, that includes more than 110,000 nodes, has among its most representative users many journalists ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]), two newspapers (La Repubblica, Linkiesta) and a politician ([PERSON]), whose stances toward the topic are generally recognisable to be positive, in favour of welcoming migrants in need. The other communities are significantly smaller but still very much involved in the debate. Community RT2 has indeed the highest number of posts with the hashtag #migranti (62,980 posts), even though it is more than three times smaller than RT1 (34,174 accounts in RT2, and 116,831 in RT1). The highest in-degree nodes of this community correspond to people with a completely opposed view with respect to principal members of RT1, being politicians, newspapers and celebrities who are publicly and vocally against foreign immigrants, often in close liaison with nationalist and right-wing parties, such as [PERSON], the extreme-right FdI party leader. Quite interestingly, RT3 (27,845 accounts, 4575 posts) and RT4 (9553 accounts, 8887 posts) can be linked to the two government parties, such as, respectively, Lega and M5S. In fact, we can easily find the Lega's leader ([PERSON]) official account, as well as some supporters (e.g., Lega--[PERSON]  
Figure 5: In-degree distribution of the top 10 nodes in each community. The degree centrality values are normalised by dividing by the maximum possible degree. We can see how a relatively smaller community, like RT3, sets the scale for all the others thanks to the presence of [PERSON], one of the most active Italian politicians on social media. Premier, [PERSON], [PERSON]), or other Lega's politicians (e.g, [PERSON]) in RT3; also, in RT4 we find some famous M5S politicians (e.g., [PERSON], [PERSON], [PERSON]), the official M5S party account, some journalists (e.g., [PERSON], [PERSON]) and a newspaper (il Fatto Quotidiano) that covered extensively the government internal debate on this topic. Finally, RT5's most retweeted accounts are linked to news outlets (SkyTg24, ANSA, Tgcom24, RaiNews, Agora Estate, Agi Agenzia Italia, Adkronos, Dagospia, Ultime Notizie, il Messaggero). If we take a look at the in-degree distributions, we find something even more interesting. [PERSON]'s account (RT3) is by far the most retweeted in the migration debate, followed by [PERSON] (RT2) and [PERSON] (RT1).