A Survey on Classification of Rumors on Social Media Using Machine Learning
DOI:
https://doi.org/10.26438/ijcse/v8i4.136140Keywords:
Rumor detection,, social network, machine Learnin, fake, NLPAbstract
Due to recent mobile technology advances, consumers have 24* 7 accesses to social networks. With regard to knowledge gaps, the dissemination of misinformation is closely linked, particularly when the data is published slowly, often as unverified data. A significant investigation is done in online social media, particularly micro-blogging websites, automatically detect rumors. Recent research on the follow-up of disinformation in social media has explored such terminology. This article will present an overview of social media rumor detection research including various types of rumor classification available in order to recognize the rumor and class text. In this survey paper we will also highlight the features of classification algorithms like Naïve Bayes, Support Vector Machine, Logistic Regression and K-Nearest Neighbor.
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