Detecting Fake News in Social Media Using Voting Classifier

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 five 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 classification algorithms. The experimental results show that the proposed framework outperforms the existing works in terms of accuracy by 0.2% for the ISOT dataset.

Automated summary

The proliferation of social media and blogs has led to the spread of fake news, highlighting the importance of utilizing machine learning algorithms for detection. A framework based on feature extraction and selection algorithms was proposed, evaluated using multiple datasets and performance metrics.