تجاوز إلى المحتوى الرئيسي

Detecting Fake News in Social Media Using Voting Classifier

Author name : OSAMA MAHMOUD OUDA
Publication Date : 2021-12-01
Journal Name : IEEE Access

Abstract

The availability of social media, blogs, and websites to everyone creates a lot of problems. False news is a critical issue that can affect individuals or entire countries. Fake news can be created and shared all over the world. The 2016 presidential election in the United States illustrates that problem. As a result, controlling social media is essential. Machine learning algorithms help to detect fake news automatically. This article proposes a framework for detecting fake news based on feature extraction and feature selection algorithms and a set of voting classifiers. The proposed system distinguishes fake news from real news. First, we preprocessed the data taking unnecessary characters and numbers and reducing the words in the dictionary (lemmatization). Second, we extracted some important features by using two types of feature extraction, the term frequency-inverse document frequency technique and the document to vector algorithm, a word embedding technique. Third, the extracted characteristics were reduced with the help of the chi-square algorithm and the analysis of the variance algorithm. We used three data sets that are published online: Fake-or-Real-News, Media-Eval, and ISOT.We used ve performance metrics to evaluate
the proposed framework: accuracy, the area under the curve, precision, recall, and f1-score. Our system achieved 94.6% of accuracy for the Fake-or-Real dataset. For the Media-Eval dataset, the system achieved 92.3% of accuracy. For the ISOT dataset, the system achieved 100% of accuracy. We contrast the proposed framework with several other classication algorithms. The experimental results show that the proposed framework outperforms the existing works in terms of accuracy by 0.2% for the ISOT dataset.

Keywords

Fake news, news classication, voting classier, term frequency-inverse document frequency, chi-square

Publication Link

https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9632574

Block_researches_list_suggestions

Suggestions to read

HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things
Ahlem . Harchy Ep Berguiga
Generalized first approximation Matsumoto metric
AMR SOLIMAN MAHMOUD HASSAN
Structure–Performance Relationship of Novel Azo-Salicylaldehyde Disperse Dyes: Dyeing Optimization and Theoretical Insights
EBTSAM KHALEFAH H ALENEZY
“Synthesis and Characterization of SnO₂/α-Fe₂O₃, In₂O₃/α-Fe₂O₃, and ZnO/α-Fe₂O₃ Thin Films: Photocatalytic and Antibacterial Applications”
Asma Arfaoui
تواصل معنا