Fake news detection using deep learning integrating feature extraction, natural language processing, and statistical descriptors

Madani, Mirmorsal; Motameni, Homayun; Mohamadi, Hosein

doi:10.1002/spy2.264

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…Madani et al [23] proposed a model for detecting fake news using feature extraction and machine learning algorithms. The sorted news samples are sent to long short-term memory and classical machine learning algorithms for detection.…”

Section: Related Workmentioning

confidence: 99%

TwIdw—A Novel Method for Feature Extraction from Unstructured Texts

Nagy

Kapusta

2023

Applied Sciences

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This research proposes a novel technique for fake news classification using natural language processing (NLP) methods. The proposed technique, TwIdw (Term weight–inverse document weight), is used for feature extraction and is based on TfIdf, with the term frequencies replaced by the depth of the words in documents. The effectiveness of the TwIdw technique is compared to another feature extraction method—basic TfIdf. Classification models were created using the random forest and feedforward neural networks, and within those, three different datasets were used. The feedforward neural network method with the KaiDMML dataset showed an increase in accuracy of up to 3.9%. The random forest method with TwIdw was not as successful as the neural network method and only showed an increase in accuracy with the KaiDMML dataset (1%). The feedforward neural network, on the other hand, showed an increase in accuracy with the TwIdw technique for all datasets. Precision and recall measures also confirmed good results, particularly for the neural network method. The TwIdw technique has the potential to be used in various NLP applications, including fake news classification and other NLP classification problems.

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Section: Related Workmentioning

confidence: 99%

TwIdw—A Novel Method for Feature Extraction from Unstructured Texts

Nagy

Kapusta

2023

Applied Sciences

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“…Third-party fact-checking services can be effective, but they may not cover all the news items posted on social media platforms (Oeldorf-Hirsch et al, 2020;Ardevol-Abreu, Dwlponti & Rodriguez-Wanguemert, 2020). Machine learning techniques, including Natural Language Processing (NLP) and Deep Learning, have been utilized to detect fake news spam emails in a cloud (Althubiti, Alenezi & Mansour, 2022; and overhead catenary (Madani, Motameni & Mohamadi, 2022;Zong, Wang & ZhiboWan, 2022;Efanov et al, 2016). While these approaches have shown promise, they may not always be practical, mainly when dealing with news items not in English (Zong & Wan, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Technique for Detection of Social Media Fake News

Arowolo

Misra

Ogundokun

2023

International Journal on Semantic Web and Information Systems

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The emergence of the Internet and the growing development of online platforms (like Facebook and Instagram) opened the way for disseminating information that hasn't been experienced in the history of mankind earlier. Consumers generate and share more information and a massive amount of data than ever with the growing utilization of social media sites, many of which are deceptive with little relevance to reality. A daunting task is the automated classification of a text article as misleading or misinformation. To see the latest news alerts, individuals often utilize e-newspapers, Twitter, Instagram, Youtube, and many more. Fake news created on social media can lead to uncertainty amongst individuals and psychiatric illness. We may detect that news obtained based on machine learning techniques is either true or false. This study proposes a machine learning technique to detect fake news by carrying out filtration on social media data, classifying the preprocessed data using a machine learning algorithm, evaluating the developed system, and evaluating the results.

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An improved transformer‐based model for detecting phishing, spam and ham emails: A large language model approach

Jamal,

Wimmer,

Sarker

2024

Security and Privacy

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Phishing and spam have been a cybersecurity threat with the majority of breaches resulting from these types of social engineering attacks. Therefore, detection has been a long‐standing challenge for both academic and industry researcher. New and innovative approaches are required to keep up with the growing sophistication of threat actors. One such illumination which has vast potential are large language models (LLM). LLM emerged and already demonstrated their potential to transform society and provide new and innovative approaches to solve well‐established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic‐based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential to profoundly impact the society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine‐tuning the BERT family of models to specifically detect phishing and spam emails. We demonstrate our fine‐tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. Moreover, IPSDM consistently outperforms the baseline models in terms of classification accuracy, precision, recall, and F1‐score, while concurrently mitigating overfitting concerns.

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Fake news detection using deep learning integrating feature extraction, natural language processing, and statistical descriptors

Cited by 7 publications

References 49 publications

TwIdw—A Novel Method for Feature Extraction from Unstructured Texts

TwIdw—A Novel Method for Feature Extraction from Unstructured Texts

A Machine Learning Technique for Detection of Social Media Fake News

An improved transformer‐based model for detecting phishing, spam and ham emails: A large language model approach

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