Kamal Singh

Fake news detection using machine learning ensemble methods

• Authors Details :  
• Iftikhar Ahmad,  
• Muhammad Yousaf,  
• Suhail Yousaf,  
• Muhammad Ovais Ahmad

Journal title : Complexity

Publisher : Hindawi Limited

Print ISSN : 1076-2787

Page Number : 1-11

Journal volume : 2020

0.9K Views Research reports

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use a machine learning ensemble approach for the automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners. The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in human history before. Besides other use cases, news outlets benefitted from the widespread use of social media platforms by providing updated news in near real-time to its subscribers. The news media evolved from newspapers, tabloids, and magazines to a digital form such as online news platforms, blogs, social media feeds, and other digital media formats. It became easier for consumers to acquire the latest news at their fingertips. Facebook referrals account for 70% of traffic to news websites. These social media platforms in their current state are extremely powerful and useful for their ability to allow users to discuss and share ideas and debate over issues such as democracy, education, and health. However, such platforms are also used with a negative perspective by certain entities commonly for monetary gain and in other cases for creating biased opinions, manipulating mindsets, and spreading satire or absurdity. The phenomenon is commonly known as fake news.

Article DOI & Crossmark Data

DOI : https://doi.org/10.1155/2020/8885861

Article Subject Details

• Computer Science
• Computer Science Applications

Article Keywords Details

• Fake News Detection
• Fake News Detection Using Machine
• Fake News Detection Using Machine Learning
• Fake News Detection Using Machine Learning Ensemble Methods

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