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Source Reliability

Measure how reliable a source is in surfacing valuable information

Image Description

Know how reliable an individual source of information is without bias and better than a human. Across the internet, there are millions of sources of information, some popular and many lesser-known. However, even if a source has low notoriety, it does not mean it is any less reliable. By modeling numerous factors including recency and propagation, Accrete has developed a dynamic scoring algorithm for scoring the reliability of an individual source, independent of its popularity. Realizing reliable sources are publishing highly accurate information with greater consistency and far in advance of market leaders opens a gateway to an untapped resource of ‘superstars’. Reliability scoring enables you to empirically understand how reliable a source of information is across time, decoupled from biases, and better than a human.

Key Features

  • Bias-Free Analysis -

    Factors like popularity skew the truth regarding the quality of information a source is sharing. With source reliability, these elements are eliminated which levels the playing field

  • Dynamic Scoring -

    A source's reliability will vary through time. By building an algorithm that accounts for this variation the accuracy of the score is far superior

  • Informed Decisions -

    By de-coupling bias and empirically measuring a source's reliability, end-users can make decisions based on the value of the content and get ahead of the competition

How It Works

The reliability of a source can be explained by two factors,

1. Recency: The elapsed time between specific information catered to a particular target, provides us with an important metric on the reliability of the sources. If a ”high-reliability” source takes a long time to make a statement as compared to a ”low-reliability” source, this implies that the informational-α has already been disseminated by the time this happens and therefore is of no use.

2. Propagation Count: The content’s informational reliability, which in turn reflects the source’s reliability provides us with a second metric to dynamically change a source’s reliability scoring. In other words, if a piece of initial information is followed by many such pieces later, there is a high probability that this source had accurate information, as opposed to content that goes away quickly.

While Recency measures how fast some information is propagating, Propagation Count measures how many sources have published the same content. A combination of the above concepts gives a way to understand the source characteristics in a dynamic fashion that captures the informational-α with maximal probability.

data = [
{ " Source " :
[ " Twitter.ALAN-TURING" ]
response = requests.post (ACCRETE.RELIABILITY , data = data, time = CURRENT)
return (response.json ())
result : " "
 [ 2.7 ]
" "

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