How social media is influencing politics
The following two sources are relevant both individually, and together. The first article is actually a gallery of rendered graphs representing content divides in social media, including some that illustrate differences in political interest on Twitter. These images are not so different from some of the examples we saw in the intro lecture at the start of the semester, and really emphasize how many examples there are of content-related separations in social networks. This article also makes a key commentary on how these divisions can be exploited to influence large audiences, especially with key “nodes” who have large amounts of followers; bot accounts can interact with these key nodes and make big impact even though the bot itself does not have many followers. The second article details the interactions between social media representatives and the lawmakers on Capitol Hill, and is related to the first article because its core message is about how influential social media is, particularly in regards to politics. The question we can then pose is this: “is it social media’s job to normalize content?” Because social networks tend to amplify ideas within small clusters of friends and family, often times even a non-partisan algorithm cannot help but provide its users content that may be skewed.
(the first) https://www.technologyreview.com/s/611807/this-is-what-filter-bubbles-actually-look-like/
(the second) https://www.newyorker.com/news/dispatch/facebook-and-twitters-rehearsed-dance-on-capitol-hill
Both these articles are examples of how powerful social networks are because of their structure. Clusters of nodes in a social network often exchange ideas that are similar in content or voice similar opinions (i.e. echo each other), which in turn strengthens the beliefs of those in the cluster. Local bridges are far and few between in social networks (in comparison to the number of connections in clusters), and are therefore easy targets to exploit for influence and access to other clusters.