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Machine Learning Approaches for Rumor Detection on Social Media Platforms: A Comprehensive Survey
Gongane V.U., Munot M.V.,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 858
Pages: 649 - 663
The inescapable utilization of Internet and social media has opened doors of social event continuous data across the globe in an exceptionally limited capacity to focus time. Social media networks are becoming a focal point for disseminating news that take the form of fake news and rumors. A rumor refers to a statement or a post that is false and is circulated fast on social media without verifying its truthfulness. Rumors have an adverse effect not only on an individual, but also it extends to the entire society. While manual detection and screening of rumors are becoming a dire need, automatic detection of rumors is an emerging research with various machine learning algorithms. The paper summarizes the utility of various machine learning algorithms for automatic rumor detection. Various steps like feature engineering and classification are also thoroughly studied and detailed in this paper. This paper coins to further research directions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalLecture Notes in Electrical Engineering
PublisherSpringer Science and Business Media Deutschland GmbH
Open AccessNo