Israel, Gaza, War & Data – social networks and the art of personalizing propaganda by Gilad Lotan.
From the post:
It’s hard to shake away the utterly depressing feeling that comes with news coverage these days. IDF and Hamas are at it again, a vicious cycle of violence, but this time it feels much more intense. While war rages on the ground in Gaza and across Israeli skies, there’s an all-out information war unraveling in social networked spaces.
Not only is there much more media produced, but it is coming at us at a faster pace, from many more sources. As we construct our online profiles based on what we already know, what we’re interested in, and what we’re recommended, social networks are perfectly designed to reinforce our existing beliefs. Personalized spaces, optimized for engagement, prioritize content that is likely to generate more traffic; the more we click, share, like, the higher engagement tracked on the service. Content that makes us uncomfortable, is filtered out.
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You are familiar with the “oooh” and “aaah” social network graphs. Interesting but too dense in most cases to be useful.
The first thing you will notice about Gilad’s post is that he is making effective use of fairly dense social network graphs. The second thing you will notice is the post is one of the relatively few that can be considered sane on the topic of Israel and Gaza. It is worth reading for its sanity if nothing else.
Gilad argues algorithms are creating information cocoons about us “…where never is heard a discouraging word…” or at least any that we would find disagreeable.
Social network graphs are used to demonstrate such information cocoons for the IDF and Hamas and to show possible nodes that may be shared by those cocoons.
I encourage you to read Gilad’s post as an illustration of good use of social network graphics, an interesting analysis of bridging information cocoons and a demonstration that relatively even-handed reporting remains possible.
I first saw this in a tweet by Wandora which read: “Thinking of #topicmaps and #LOD.”