Bitpress: An Open Protocol for Tracking the Credibility of News
medium.com
medium.com
Our algorithm works by first identifying a set of powernodes (right now, about 1% of the total nodes). Each of these powernodes is then given the ability to "vote" on whether content is trustworthy or not.
Using the MIT Media labs tags I can then quantify what % of the total trust is held by the liberals and the conservatives. Currently, there amount of trust between both sides is roughly equal, though on the liberal side most of the trust tends to be in the "left-center", while on the conservative side it is mostly held by more extremist outlets such as fox news, breitbart, and the daily caller.
Interestingly, the most trusted content is often linked to by both parties, and through the classifications its very easy to identify which articles are partisan/are only being linked to by one side.
The conservative outlets don't appear to often support each other through collectively linking to bad content. Totaling up all of the conservative votes - the number one trusted source is unsurprisingly fox news, but the number 2 and number 3 sources are the Washington Post and the Times.
https://imgur.com/O05eHfX (Most trusted overall)
https://imgur.com/FCoJrWd (Most trusted conservative)
https://imgur.com/SotcwD0 (Most trusted center)
https://imgur.com/kg9dmou (Most trusted liberal)
Its interesting that even with several hyperpartisan conservative outlets given a "seat" at the voting table, these sources still bubble to the top. Compare this to the results from Facebook's recent attempt to start ranking publisher trust, using users' ratings: https://twitter.com/kevinroose/status/993502674233577479
Does this not all but ensure that the status quo will be maintained and reinforced? "The 1%" can create a moat to keep unapproved sources out the realm of "trusted" opinion. Hard to imagine any genuinely dissenting outlets gaining any traction that way. Am I misunderstanding things?
1) The list of powernodes is derived from running a modified version of Eigentrust on the network, and selecting the top n nodes. This is run at the publisher/domain level.
2) After identifying the powernodes, they are used to rank news at the article level, based on how many powernodes are linking to a source.
Therefore the powernodes are selected based on the entire graph, and can change over time. Testing our ideas on the current media landscape seems to show the algorithm does a good job of identifying local maxima.
For example: Our current list of around 60 powernodes was generated from a graph of around 3.5 million articles from the last 3 years. It includes sites bitcoin.com and coindesk, two sites that are relatively new compared to several of the larger incumbents.
From the left, it includes sites like salon, vox, and msnbc.
From the right it includes sites like breitbart, the daily caller, and the washington examiner.
All trustless cryptography based systems have a threshold of malicious participants they can tolerate before failing. Bitcoin, for example, has a well known threshold of 51%. The media, however, is diverse enough that the idea they would all somehow collude with one another to game the system seems unlikely.