TY - JOUR
T1 - Certain Investigation of Fake News Detection from Facebook and Twitter Using Artificial Intelligence Approach
AU - Setiawan, Roy
AU - Ponnam, Vidya Sagar
AU - Sengan, Sudhakar
AU - Anam, Mamoona
AU - Subbiah, Chidambaram
AU - Phasinam, Khongdet
AU - Vairaven, Manikandan
AU - Ponnusamy, Selvakumar
N1 - Funding Information:
This work has been partly supported by the European Regional Development Fund under the project "Advanced technologies in power plants and rail vehicles", and by the Croatian Science Foundation through the project 3CON (94020-2014). The authors would also like to thank Mr. Rajko Ku?en, M.Sc. (Solvis Ltd.) for provided experimental data obtained by experiments with the artificial sun, to Meteorological and Hydrological Service, Croatia, for provided predictions of solar irradiance components, and to two anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/11
Y1 - 2022/11
N2 - The news platform has moved from traditional newspapers to online communities in the technologically advanced area of Artificial Intelligence. Because Twitter and Facebook allow us to consume news much faster and with less restricted editing, false information continues to spread at an impressive rate and volume. Online Fake News Detection is a promising field in research and captivates the attention of researchers. The sprawl of huge chunks of misinformation in social network platforms is vulnerable to global risk. This article recommends using a Machine Learning optimization technique for automated news article classification on Facebook and Twitter. The emergence of the research is facilitated by the strategic implementation of Natural Language Processing for social forum fake news findings in order to distort news reports from non-recurrent outlets. The relent from the study is outstanding with text document frequency words, which act as extraction technique’s attribute, and the classifier is acted upon by Hybrid Support Vector Machine by achieving 91.23% accuracy.
AB - The news platform has moved from traditional newspapers to online communities in the technologically advanced area of Artificial Intelligence. Because Twitter and Facebook allow us to consume news much faster and with less restricted editing, false information continues to spread at an impressive rate and volume. Online Fake News Detection is a promising field in research and captivates the attention of researchers. The sprawl of huge chunks of misinformation in social network platforms is vulnerable to global risk. This article recommends using a Machine Learning optimization technique for automated news article classification on Facebook and Twitter. The emergence of the research is facilitated by the strategic implementation of Natural Language Processing for social forum fake news findings in order to distort news reports from non-recurrent outlets. The relent from the study is outstanding with text document frequency words, which act as extraction technique’s attribute, and the classifier is acted upon by Hybrid Support Vector Machine by achieving 91.23% accuracy.
KW - Fake News
KW - Hybrid SVM
KW - Machine Learning
KW - NLP
UR - http://www.scopus.com/inward/record.url?scp=85109357141&partnerID=8YFLogxK
U2 - 10.1007/s11277-021-08720-9
DO - 10.1007/s11277-021-08720-9
M3 - Article
AN - SCOPUS:85109357141
SN - 0929-6212
VL - 127
SP - 1737
EP - 1762
JO - Wireless Personal Communications
JF - Wireless Personal Communications
IS - 2
ER -
