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Springer Data Science for Fake News Detection - Springer Series 42

Amazon

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Description

This comprehensive book from Springer explores fake news detection through survey articles and expert perspectives across multiple disciplines. The work combines tutorial-style content capturing recent advancements with insights from domain specialists, recognising that tackling fake news requires interdisciplinary collaboration. The book addresses how data science subfields including graph analytics contribute to developing effective detection methods and tools. It bridges academic research and practical application, offering readers both foundational knowledge and cutting-edge approaches to understanding and combating misinformation in digital environments.

Key Features

The Information Retrieval Series volume 42 provides structured coverage of fake news detection methodologies and current research directions.

Tutorial-style survey articles present advancements in fake news detection from multiple perspectives and specialisations.

Expert contributors from various disciplines share domain knowledge essential for shaping next-generation detection methods.

Graph analytics and other data science subfields are explored as key techniques for identifying and analysing fake news patterns.

Springer's publication ensures rigorous academic standards and peer-reviewed content on misinformation and detection strategies.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 April 2021
Listed Since
26 September 2020

Barcode

No barcode data available

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