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Fake News Data Exploration and Analytics
- Source :
- Electronics, Vol 10, Iss 2326, p 2326 (2021), Electronics, Volume 10, Issue 19
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models<br />random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.
- Subjects :
- fake news
TK7800-8360
Computer Networks and Communications
Computer science
Big data
detection
Newspaper
big data
analytics
Social media
natural language processing
Electrical and Electronic Engineering
Information retrieval
business.industry
logistic regression
Decision tree learning
Sentiment analysis
TF-IDF
Unstructured data
machine learning
Hardware and Architecture
Control and Systems Engineering
Analytics
Signal Processing
data exploration
The Internet
InformationSystems_MISCELLANEOUS
Electronics
business
unstructured data
random forest
Subjects
Details
- ISSN :
- 20799292
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Electronics
- Accession number :
- edsair.doi.dedup.....7381a02b3dee458b0152c68034ead303