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Source Popularity

Differentiate how popular a source of information is without bias

Image Description

Understand the popularity of a source of information better than a human, without bias. This function uses a variety of parameters and a computational model that accounts for the average amount of traffic, individual bounce rate, and average time spent on the source. The popularity score is made more accurate by inputting data that is specific to the source only, factors including the relevancy of the source to the news, social popularity, and influence. By blending all of these data points the individual popularity of a source can be quantified giving you complete transparency on a source’s popularity.

Key Features

  • Continuous accurate assessment -

    The model considers a wide variety of factors that are dynamically adjusted as part of the feedback process which refines and increases the accuracy of the algorithm

  • Regression modeling -

    Various regression models are assigned to the various factors which are continuously refined by the Accrete development team improving the scoring over time

  • Secure and reliable -

    Accrete hosts our knowledge functions using global computing infrastructure that is reliable, scalable and secure. Service redundancy is guaranteed with 99.999% uptime.

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

Result: “”



How It Works

The model looks at different factors to identify the source popularity based on the source type. A social media source’s popularity is calculated on the basis of the number of followers, number of shares, number of likes, number of comments, and nested count of the reach of the post. Other source popularity is based on the type of source, Alexa rank of the source, number of visits, content type, and comments. The model keeps refining the weights for different factors of the model to keep improving the popularity score using a continuous learning model based on user feedback.


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