A Review of Fake News Detection Methods using Machine Learning

Choudhary, Murari; Jha, Shashank; Prashant, .; Saxena, Deepika; Singh, Ashutosh Kumar

doi:10.1109/incet51464.2021.9456299

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…Social media continues to change with different developments every day (McCorkindale & DiStaso, 2014). This change also creates small difficulties for individuals (Choudhary et al, 2021). New learning environments that encourage brain-based learning, with their dynamic structure, direct today's learners to challenge themselves every day and use their brains as parallel processors.…”

Section: The Brain Is a Parallel Processormentioning

confidence: 99%

An Investigation of Brain-Based Learning Principles in Today's Changing Conditions

Korkmaz

2024

Anadolu Üniversitesi Eğitim Fakültesi Dergisi

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The increasing prevalence of brain research and the fact that the findings reveal important arguments about the learning process have led to the proliferation of brain-based learning studies. Brain-based learning principles, introduced by Caine and Caine in 1991, are frequently used in the literature and shed light on educational research. However, in today's changing conditions, educational environments have remarkably differentiated, the opportunities for learners have increased more than ever before, threats such as the Covid-19 pandemic and natural disasters have directly affected education, and technological advances have made a breakthrough with artificial intelligence. There are studies evaluating 12 accepted and frequently used brain-based learning principles in the literature, but there is no review study that addresses brain-based learning principles in today's changing conditions. Therefore, this article aims to examine the principles of brain-based learning in today's changing conditions. Increasing threats in the rapidly changing world pose a risk to brain-based learning. Also, increasingly common helicopter parenting and artificial intelligence hinder brain-based learning, although they have some advantages. However, on the other hand, the fact that technological developments make educational environments richer and increase the opportunities for learners greatly facilitates brain-based learning. Thus, brain-based learning principles have become much more meaningful and gained great importance in today's world.

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Section: The Brain Is a Parallel Processormentioning

confidence: 99%

An Investigation of Brain-Based Learning Principles in Today's Changing Conditions

Korkmaz

2024

Anadolu Üniversitesi Eğitim Fakültesi Dergisi

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“…Their work is crucial in understanding the relative performance of various models and selecting the most effective ones. [15] provided a review of fake news detection methods using machine learning, offering insights into the evolution of detection techniques and underscoring the importance of continuous research in this domain. Siddikk et al (2022) [16] proposed a machine learning-based framework, termed FakeTouch, for detecting fake news.…”

Section: Location-independent Approachesmentioning

confidence: 99%

A Machine Learning-based Approach for Detecting Fake News in Online Media

Pathan,

Bhavsingh,

Alipanahi

et al. 2023

IJCERT

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The proliferation of fake news in online media poses a significant challenge to information credibility, necessitating robust detection mechanisms. This study addresses the growing concern by proposing a machine learningbased approach, aiming to discern genuine news from fabricated stories effectively. We identify the deficiencies in existing systems, particularly their failure to adapt to the sophisticated and evolving nature of misinformation and their limited ability to process the subtleties of deceptive content. To bridge this gap, we explore the efficacy of three distinguished machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Decision Trees, each with its unique strengths in text classification. Our methodology encompasses a comprehensive feature engineering process to capture the stylistic and semantic nuances of textual data, followed by rigorous model development and validation. We assess model performance through an array of metrics, including accuracy, precision, recall, and F1 score, to ensure a multifaceted evaluation of each algorithm's capabilities. The empirical analysis demonstrates that the SVM model achieves the highest accuracy, at 36.5%, marking it as the most proficient in our comparative study. The study's significant contribution lies in its detailed analytical approach, providing insights into the models' performance and laying the groundwork for future advancements in the field. Our findings not only enhance the current understanding of fake news detection but also pave the way for the development of more sophisticated and reliable detection systems. The overarching achievement of this research is the advancement toward a more trustworthy online information ecosystem, where the veracity of content can be ascertained with greater confidence.

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“…• Stance-based: It presents factual statements so that their context and intention are altered. • Knowledge-based: Such posts provide comprehensive (so-called) explanations for unaddressed situations, leading individuals to assume the information is genuine-for instance, natural therapies for high blood sugar levels in humans [64].…”

Section: B Categories Of Fn (Fake News)mentioning

confidence: 99%

Fake News Detection System Using Featured-Based Optimized MSVM Classification

et al. 2022

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Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country's status or overstates the expense of special functions for a government, destroying democracy in certain countries, such as in the Arab Spring. Associations such as the ''House of Commons and the Crosscheck project'' address concerns such as publisher responsibility. However, since they rely entirely on manual detection by humans, their coverage is minimal. This is neither sustainable nor possible in a world where billions of items are withdrawn or posted every second. The paper produces a deep study on past research work on fake news detection on the selected data-sets and proposes an algorithm with Multi-layered Principal Component Analysis for feature selection followed by firefly-optimized algorithm. Multi-Support Vector Machines(MSVM) are finally used to classify the news. We used ten different data-sets for testing the proposed algorithm. As the number of features in the data-sets are more, feature extration and selection methods help to improve the accuracy in respective data-sets. Only the datasets having less number of features gave a lower performance on our feature extraction algorithms. INDEX TERMSFake news, rirefly, long short-term memory (LSTM), multi support vector machine (MSVM).

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A Review of Fake News Detection Methods using Machine Learning

Cited by 15 publications

References 18 publications

An Investigation of Brain-Based Learning Principles in Today's Changing Conditions

An Investigation of Brain-Based Learning Principles in Today's Changing Conditions

A Machine Learning-based Approach for Detecting Fake News in Online Media

Fake News Detection System Using Featured-Based Optimized MSVM Classification

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