Detecting Fake News Using Machine Learning Algorithms

被引：3

作者：

Bharath, G. ^{[1
]}

Manikanta, K. J. ^{[1
]}

Prakash, G. Bhanu ^{[1
]}

Sumathi, R. ^{[1
]}

Chinnasamy, P. ^{[2
]}

机构：

[1] Kalasalingam Acad Res & Educ, Dept Comp Sci & Engn, Krishnankoil, Tamil Nadu, India

[2] Sri Shakthi Inst Engn & Technol, Dept Informat Technol, Coimbatore, Tamil Nadu, India

来源：

2021 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI) | 2021年

关键词：

Fake news; STEM; Naive Bayes; Machine learning; Social media; Twitter APJ; Sentimation analysis;

D O I：

10.1109/ICCCI50826.2021.9402470

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Online media cooperation particularly the word getting out around the organization is an incredible wellspring of data these days. From one's point of view, its insignificant effort, direct access, and speedy scattering of data that lead individuals to watch out and global news from web sites. Twitter being a champion among the most notable progressing news sources moreover winds up a champion among the most prevailing news emanating mediums. It is known to cause broad damage by spreading pieces of tattle beforehand. Therefore, motorizing fake news acknowledgment is rudimentary to keep up healthy online media and casual association. We proposes a model for perceiving manufactured news messages from twitter posts, by making sense of how to envision exactness examinations, considering automating fashioned news distinguishing proof in Twitter datasets. Subsequently, we played out a correlation between five notable Machine Learning calculations, similar to Support Vector Machine, Naive Bayes Method, Logistic Regression and Recurrent Neural Network models, independently to exhibit the effectiveness of the grouping execution on the dataset. Our exploratory outcome indicated that SVM and Naive Bayes classifier beats different calculation.

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