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Viral Influence

Quantify the interactions between users on social media platforms

Image Description
Overview

Knowing how influential a user and their content is very difficult to quantify. Every social media platform Twitter, YouTube, TikTok, etc. employ similar models whereby users are encouraged to post information and follow other users. This generates countless interactions between users through likes, comments, reposts, shares, and various other actions. To quantify the value of interactions, Accrete has developed a model that counts interactions and calculates the popularity of interacting users, followers, followers-of-followers, and so on. By looking beyond first degree interactions Accrete can model the sphere of influence and quantify the virality of the individual users.

Key Features

  • Real-time Assessment -

    Don't rely upon quantitative values; the number of likes, shares or follower counts. Compute a user's viral influence in real-time.

  • 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

  • Dynamic Scoring -

    A user's viral influence score is continuously recalculating as new interactions and activity takes place. By building an algorithm that accounts for variation the accuracy of the score is superior.

  • Informed Decisions -

    De-couple bias and empirically measure a user's viral influence. Make decisions based on the true value of the user and increase your Return-On-Investment.

  • o Down-Time -

    Unlike a human researcher, a machine is always switched on. With 99.999% uptime the A.I continuously scours for relevant information

Illustration
How It Works

The viral influence of a user is calculated based on ‘impact’ factors, this is derived from the qualitative engagement of their own content. For example, the viral influence score for an artist on a music-centric platform calculates the impact of their tracks upon immediate and nested user networks. To calculate the impact of user-generated content the algorithm considers factors such as likes, reposts, shares, comments or other pertinent actions. A network tree is constructed for each piece of content that considers user's activity i.e. likes, downloads, etc. Within a network tree, the root node represents the user and the child nodes are interacting users. Once this network tree is constructed, using Accrete’s Source Popularity knowledge function these interactions are scaled to calculate the impact. The viral influence score of the user is the average impact of all of the user-generated content.

Demo
data = [

{ " userid " :39018976, " source " :”SoundCloud” }

]

response = requests.post (ACCRETE.INFLUENCE , data = data)

return (response.json ())

""

result : " "

37.5  ]

" "