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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Comparing Supervised & Unsupervised ML for Fake News Detection

Comparing Supervised & Unsupervised ML for Fake News Detection

Kaur, Sufanpreet
Ranjan, Sandeep

Paperback

General Computers

ISBN10: 6208116228
ISBN13: 9786208116224
Publisher: LAP Lambert Academic Publishing
Published: Sep 12 2024
Pages: 64
Weight: 0.23
Height: 0.15 Width: 6.00 Depth: 9.00
Language: English
This investigation aims to develop a robust framework for detecting false information by comparing supervised and unsupervised machine learning algorithms. Unsupervised algorithms identify patterns without pre-labeled data, while supervised algorithms use labeled datasets to guide detection. The evaluation focuses on accuracy, precision, recall, and F1 score to assess each algorithm's effectiveness. The study details dataset composition, preprocessing techniques, and the strengths and limitations of each method. It utilizes Kaggle's dataset, featuring various news stories classified through meticulous verification, including real, fraudulent, and mixed authenticity levels. This research emphasizes the importance of precise labeling and preprocessing, aiming to enhance the development of effective fake news detection systems using advanced machine learning and natural language processing techniques.

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