fake news detection using machine learning research papers ieee

The phenomenon of Fake news is experiencing a rapid and growing progress with the evolution of the means of communication and Social media. Fake news detection is an emerging research area which is gaining big interest. It faces however some challenges due to the limited resources such as datasets and processing and analysing techniques. In this work, we propose a system for Fake news ...

Machine learning has played a vital role in classification of the information although with some limitations. This paper reviews various Machine learning approaches in detection of fake and fabricated news. The limitation of such and approaches and improvisation by way of implementing deep learning is also reviewed.

Automatic fake news identification involves determining the authenticity of news reports. The detection of fake news is a crucial but complicated task in natural language processing. Our aim is to design and train model using machine learning techniques for automatic detection of fake news. The suggested model achieves encouraging results.

This research describes a variety of machine learning algorithms for identifying bogus and fabricated news. The key goal of this paper is to demonstrate which machine learning algorithm gives maximum accuracy and it was found that the Decision tree gives an accuracy of 99.97 % among all the other algorithms.

The research area of fake news detection is gaining interest but at the same time it involves a number of challenges due to unavailability of quality resources such as datasets, published literature etc. ... such as Buzzfeed, Politifact, CREDBANK, FakeNewsNet and various classification techniques such as Support Vector Machine (SVM), K-Nearest ...

Machine Learning algorithms such as Naive Bayes, Passive Aggressive Classifier and Deep Neural Networks have being used on eight different datasets acquired from various sources. The paper also includes the analysis and results of each model. The arduous task of detection of fake news can be made trivial with the usage of the right models with ...

This work helps us to detect the accuracy of the fake news using different classification techniques. Fake news is significantly affecting our social life, in fact in every field mainly in politics, education. In this work, we have presented the solution for Fake news problem by implementing fake news detection model by using different classification techniques. Fake News Detection becomes ...

In Table 1 the differences with other reviews in the field of fake news detection are highlighted. Specifically, compared to other reviews, this work is the only one that does an extensive evaluation of features and models as well as their performances on multiple datasets; moreover, some reviews focus only on a subset of models (e.g. Natural Language Processing or Deep Learning) or topics (e ...

PDF | On Oct 29, 2021, Keshav Nath and others published ©2021 IEEE Study of Fake News Detection using Machine Learning and Deep Learning Classification Methods | Find, read and cite all the ...

We will also like to discuss related research areas, open topics, and future research ideas for spotting false news on social media. It is very violated towards society to saw such fake news over the internet that is going to happen every day. Our paper will help to detect fake news with the use of python and some machine learning algorithm.

Detection of online fake news using n-gram analysis and machine learning techniques. In International conference on intelligent, secure, and dependable systems in distributed and cloud environments. Springer, 127-138. Google Scholar Cross Ref; Hunt Allcott and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election.

Gilda S (2017) Evaluating machine learning algorithms for fake news detection. In: IEEE 15th Student Conference on Research and Development (SCOReD) Google Scholar Vedova MLD, Tacchini E, Moret S, Ballarin G, DiPierro M, de Alfaro L (2017) Automated online fake news detection using content and social signals. ISSN 2305-7254

Motivations. Although there have been several surveys on FND, most of them divide the existing research from the feature perspective, for example, Zhou and Zafarani (2018) categorized the approaches for detecting fake news into the four categories listed below: external knowledge-based detection methods, style-based detection methods, propagation-based detection methods, and credibility-based ...

Machine learning algorithms and. deep learn ing algorithms have been used to classify news and. detect fake news, a binary classification (real and fake), but. the quality of each model's ...

Machine learning techniques have been experimented on a range of datasets and deep learning techniques are still to be fully evaluated on the fake news detection and related tasks. 3. Proposed approach. The research on fake news detection requires a lot of experimentation using machine learning techniques on a wide range of datasets.

The authors of [] provide taxonomy of several methods for judging the reliability of information that may be divided into two major groups: techniques for spotting fake news using network analysis and linguistic cues combined with machine learning.Using a rudimentary Bayesian classifier, authors [] present an easy technique which provides a way to identify bogus news.

Kaggle was the platform identified with the most frequently used datasets, being succeeded by Weibo, FNC-1, COVID-19 Fake News, and Twitter. For future research, studies should be carried out in ...

This paper provides an approach for solving the fake news detecting issue which relies on machine learning based on granular computing. These authors use various machine learning algorithms such as k-nearest neighbors (KNN), random forests and support vector machines (SVMs) to analyze the content of news articles.

This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis.

Machine Learning. Conference PaperPDF Available. Fake News Detection Using Machine Learning approaches: A systematic Review. April 2019. DOI: 10.1109/ICOEI.2019.8862770. Conference: 2019 3rd ...

Fake news has become a major social problem in the current period, controlled by modern technology and the unrestricted flow of information across digital platforms. The deliberate spread of inaccurate or misleading information jeopardizes the public's ability to make educated decisions and seriously threatens the credibility of news sources. This study thoroughly examines the intricate ...

Different researchers are working for the detection of fake news. The use of Machine learning is proving helpful in this regard. Researchers are using different algorithms to detect the false news. Researchers in (Wang, 2017) said that fake news detection is big challenge. They have used the machine learning for detecting fake news.

Abstract. Fake news may have different meaning to different individuals. For the purpose of this paper, we will go by the definition of fake news as those reports that are bogus: The story itself is created, with no relation to realities, sources or statements. In this research on fake news detection through machine learning algorithms, we are ...

This paper. demonstrates a model and the methodolog y for. fake news detection. With the help of Machine. learning and natural language processing, it is. tried to aggregate the news and later ...

This research describes a variety of machine learning algorithms for identifying bogus and fabricated news and it was found that the Decision tree gives an accuracy of 99.97 % among all the other algorithms. Due to the sheer simple accessibility and exponential rise of data through social media networks, discriminating between false and genuine information has now become challenging. The ...

Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations.

The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence applications. Acquiring a sufficient amount of realistic defect data is challenging, especially during ...