A Semi-Supervised Learning Approach for Twitter Spam Drift detection

A Semi-Supervised Learning Approach for Twitter Spam Drift detection
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Twitter Spam Drift detection

Twitter Spam Drift detection is analyzed from live stream of twitter data. Drift detection is the problem of identifying spam over the time. That is, through the course of time and it is dynamic. Machine learning algorithms are analyzed in the project

Twitter Spam Drift detection

Which algorithms used?

Naive Bayes, Logistic regression, KNN and SVM are used for detection.

The following modules are implemented
Data Collection
Data Pre-processing
Label data using YATSI
Twitter spam prediction

What is Unique in this project?

The uniqueness of the project is that the data is dynamic and thus the spam drift can be identified over the time.

Project Demo video

More related projects on twitter data are available as follows

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