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Social Media Networks and the Israeli–Palestinian Conflict

I recently read an article about the way social media affects our perception of conflicts, specifically the Israeli-Palestinian conflict: https://medium.com/i-data/israel-gaza-war-data-a54969aeb23e

The article talks about how by representing social media as a graph, we can gain insight into how information about the conflict spreads. The author presented the following graph of Twitter handles, where larger nodes are more popular and nodes are closer together if there are more connections between them.

Twitter handles as nodes in a graph – node size indicates popularity and distance between nodes indicates how many connections they share.

This graph shows that there are two very distinct clusters of nodes, with a large gap between them. This is not surprising considering that when two nodes share many neighbors they are likely to form a connection themselves. In this way, media outlets and people with similar views are more likely to be exposed to each other than to those of differing views. This creates more clustered connections, which in turn increases the likelihood of similar connections being made. But this segregated graph structure is increased by other factors as well. Media companies use algorithms that recommend things they think users will be interested in, and people have a tendency to look for evidence that confirms their beliefs (confirmation bias) and either ignore or become angered by opposing viewpoints.

This segregated graph structure is problematic because in any conflict between large groups of people, from the violent Israeli-Palestinian conflict to the entrenched the democrat-republican schism in the U.S., cooperation is required. Yet cooperation is exactly what this kind of graph inhibits by restricting the flow of information from one side to the other. As the article points out, each side may be seeing entirely true facts, but if they each see a different subset of the whole truth, they will never understand how one could possibly come to a different conclusion from their own.

But representing a conflict’s media attention in this way can also be illuminating. How can this graph help us bring both sides together? You may notice that there actually are nodes in the middle of the graph, bridges between the clusters. The problem is that most of these nodes are small, meaning they aren’t very popular. The article suggests that this stems from middle-ground media not having a core audience that the more extreme ones have. But this graph helps us identify the our best chances of bridging the conflict: by supporting the most popular middle-ground media. In this graph, Ha’aretz is both close to the middle and relatively large. By supporting Ha’aretz, we increase the chances of spreading information between both sides. As connections to Ha’aretz (and any other large, middle-ground media) grow, the hope is that people will get more exposure both to opposing viewpoints and to other middle-ground media that Ha’aretz has connections to. Maybe by using graphs to understand our biases and identify bridges between sides, we can help resolve conflicts like the Israeli-Palestinian conflict.

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