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What methods and algorithms stand behind HypeAuditor data?
Find out more about the deep learning algorithms that we use to provide insights about influencers’ audience, detect fraud activity, and find perfect fit influencers for marketing campaigns.
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Machine learning

Machine learning is a method of data analysis that automates analytical model building. It is a branch of AI based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

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Natural Language Processing

Natural language processing or NLP is a branch of AI that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.

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Сomputer Vision

Computer vision is a field of AI that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”

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As well as deep learning, KNN, Native Bayes, and many more.

How do you detect a low-quality audience?

To detect a low-quality audience, HypeAuditor uses a specially trained ML-model, which is based on the ensemble of machine learning algorithms and uses 53+ patterns to detect suspicious accounts. As a result, it detects 95.5% of all known fraud activity, with a mean error rate of 0.73%.

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How do you detect a low-quality audience?
How do you analyze comments authenticity?

How do you analyze comments authenticity?

HypeAuditor relies on a cutting-edge Natural Language Processing algorithms to run a syntactic and semantic analysis to derive meaning from human languages, which helps to analyze comments authenticity. The algorithm also checks accounts for suspicious patterns and behavior that it has witnessed on bots and other low-quality accounts.

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How do you detect audience age and gender?

With the help of computer vision methods, HypeAuditor understands the content of images and detect audience age and gender. This helps advertisers to target better and make their campaigns more diverse.

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How do you detect audience age and gender?
How do you identify audience interests?

How do you identify audience interests?

To identify audiences’ interests, HypeAuditor uses a set of classification algorithms based on a similarity measure (including KNN, Native Bayes, and BM25).

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