3 ms·
Somehow this post takes the least interesting part of the source article[0] and draws false conclusions from it. > In the simulation, the decision whether to r
by 3np 5y ago
Somehow this post takes the least interesting part of the source article[0] and draws false conclusions from it.
> In the simulation, the decision whether to rebroadcast is random, rather than being driven by “virality” or cognitive bias, so the simulation is an optimistic one.
> It turns out that message propagation follows a power law: the probability of a meme being shared a given number of times is roughly proportional to an inverse power of that number.
So they implement a textbook model and a textbook result comes out - surprise? There’s nothing to be drawn by this.
I may share the authors sentiment but frankly this blog post is bunk.
There’re some interesting parts in the source though once you get through all the grand-standing fluff.
[0]: https://www.scientificamerican.com/article/information-overload-helps-fake-news-spread-and-social-media-knows-it/ https://www.scientificamerican.com/article/information-overl...
- d110af5ccf 5y agoThe Scientific American article does bring up a number of interesting social dynamics. However the modeling described seems bogus to me. The algorithms sound like they are incredibly oversimplified. It seems unreasonable to assume that all people interact with social media in the same way or to assume that their behavior is random. For perspective, genetic algorithms are a biased random walk and they clearly work quite well.
- ummwhat 5y agoIf it's the paper I'm thinking of then I read the original and that's not really a fair representation of what they did. What they actually did was they started with some data on things which had gone viral but could only see how many were "infected" (number of views over time). They didn't have direct access to the granular information of who had seen what and who they shared or didn't share with. They decided to work backwards and model virality for some assumed network properties and contagiousness properties and then see which of their models had the similar statistics to actual viral posts. Network topology: iirc they tested the case of a gaussian distribution of connections meaning on average 95% of nodes had n connections +- 2sigma and they tested a power law type distribution meaning some thin minority of "influencers" had connections to nearly everyone". Contagious property: they tested the case where different content had a different virality property r0 with more or less contagious content all competing at the same time. They also tested the case of sheer randomness wherein all content is equally likely to be shared and which goes viral basically depends on the luck of who's eye it catches. The end result was that a power law and random contagiousness best fit the real world statistics.
- masswerk 5y agoAlso mind that any sharing process randomly selects a few winners. There's the famous example of "What happens, if 100 persons in a room are given $100 each and give away a Dollar to one randomly chosen person in this room each tick of the clock?" [1] Distributions become even more articulated, as we introduce any kind of market to this basic model, and wins are soon off the scale. Now start to promote those winners by algorithm… The only means of counter-acting is human oversight/criticism/regulation, but, as the study shows, this reaches only that far. [1] A simple simulation may be found here: https://www.masswerk.at/misc/wealth/ https://www.masswerk.at/misc/wealth/
- deleted 5y ago[deleted]