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A survey of rumor detection methods in social networks
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Fariba Sadeghi   , Amir Jalaly Bidgoly * |
| University of Qom |
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Abstract: (2869 Views) |
Rumors, are unverified and often erroneous news that are widely propagated at the community level, discrediting or falsely increasing the trust of nodes in a network to an entity or subject. With the rise social networks in recent years, despite their positive uses, propagating rumors have become easier and more common. Rumors are a class of security challenges on social media, since a malicious node can easily disparage or isolate its goals by spreading a rumor. Therefore, rumors detection is an important challenge in soft security mechanisms such as trust and reputation. Researchers have come up with different methods for modeling, detecting and preventing rumors. In this study, rumor detection methods in social networks will be reviewed. First, we will briefly review the features used in previous research, then we will examine the approaches used and introduce the most commonly used Dataset. Finally, the challenges that exist for the future research in exploring social media to identify and resolve rumors are presented. |
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| Keywords: Rumor Detection, Social Media, Machine Learning, Neural Network |
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Full-Text [PDF 3501 kb]
(738 Downloads)
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Type of Study: Review Article |
Subject:
Special
Received: 2019/03/28 | Accepted: 2019/08/24 | Published: 2020/03/18
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