Idea: Facebook (or other social media platform) client (or browser plugin, even though I am against plugins altering rendering in principle) where the user logs in using 1-N credentials. Each page is requested from each account. The results/feed is de-duplicated, but shows content from every concurrent account, with a mechanism for an advanced user to show which accounts can see the content and which cannot (e.g. Success/fail list for each object, cross user frequency for each object, reports, etc.). This will serve the white hat to test network behaviors, understand delivery mechanisms, and show what kinds of prioritization occur for each distinctive ID (with its own variant false identity). This will allow a motivated user (black hat, to aggressive consumer/advertiser) to more effectively counter blocks, see information hidden specifically from their account, and post from random accounts (sock-puppeting). A special routine could use dictionaries, common misspellings, and timings to make a set or random number of accounts respond with similar comments (or insults against the intended target). This creates the illusion of far stronger support/rejection of an idea to influence decision makers on an order of magnitude. Of course, such a routine would need to carefully avoid comment de-duplication and use timing algorithms to pseudo-randomly respond to avoid suspicion. A certain number of clients would cross-like comments/posts, tag related accounts in discussion responses, etc. Distinctives: Route all requests via Tor (argument for browser or application level implementation) and delay them by up to 2000ms, to make it difficult to detect concurrent sessions. Implement each request via a separate session/thread, so the browser does not cross session IDs. During spare/idle time (user throttled), each related browser thread (session) will also send requests to search engines, e-commerce sites, click links, to generate non-site traffic to generate distinctive behaviors social networks can read through advertiser data mines. Goal: Avoid blocks. Test algorithmic behaviors. Create exponential influence. Create large amounts of content and/or meta-data misinformation.
Posted on: Mon, 08 Dec 2014 10:51:05 +0000
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