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Authentic Engagement

Differentiate between online bots, self-promotion, and real user-generated content

Image Description
Overview

With millions of users sharing information online, it has become imperative for analysts to understand the authenticity of each user. With the rise of troll farms and the mass automation of bots, high-levels of engagement can be manufactured. These disingenuous tools are used by organizations to spread misinformation and by people to unfairly garner fame. To account for these dynamics, Accrete's team has implemented a machine learning algorithm for Natural Language Processing that reads through comments in a semantic way. It repeats this process for each connected user up to 2 degrees away. By including the network of followers it filters out the bots and increases the accuracy of the authentic engagement score.

Key Features

  • Real-time Assessment -

    Don't rely upon quantitative values; number of likes, shares or follower counts. Compute the true authenticity of any online user profile in real-time.

  • Target Real Users -

    Filter out bots, trolls, and self-promotion. Engage with genuine users with a high-value online presence. Do this in a more nuanced way and increase your Return-on-investment.

  • Dynamic Scoring -

    A source's authentic engagement value will vary depending upon recent activity through time. By building an algorithm that accounts for this variation the accuracy of the score is far superior.

  • Informed Decisions -

    De-couple bias and empirically measure a source's authenticity. Make decisions based on the true value of the user and get ahead of the competition.

Illustration
How It Works

The machine uses a character-level based embedding learning model that converts each comment to a tensor (or an algebraic object). Character-level models are helpful in cases where the language may not have a specific linguistic structure such as in social media comments, and it can account for the use of emojis. The model is trained in an unsupervised manner on a subset of all the data available. Hereafter, the embeddings are then passed through an unsupervised clustering algorithm, which outputs 3 clusters; (1) Valid comments, (2) Self-promotional comments, and (3) Junk comments. The comments for each user are analyzed and an aggregated score is computed using Accrete's proprietary algorithm.

Demo
data = [

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

]

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

return (response.json ())

""

result : " "

0.0.85 ]

" "

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