A Tool for Fake News Detection

Asaad, Bashar Al; Eraşcu, Mădălina

doi:10.1109/synasc.2018.00064

Cited by 42 publications

(13 citation statements)

References 6 publications

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“…The use of uppercase words is different between the two datasets: in real news, the use of uppercase words is more frequent to indicate acronyms, brands, and organisations, while in fake news, uppercase words emphasise feelings, alerts, and potential warnings. used pre-training algorithms, such as bag-of-words (BoW) [23] and term frequency-inverse document frequency (TF-IDF) [24,25], for mapping cleaned texts (titles and descriptions) into numeric representations. Further features (length, counting, and binary) were also extracted from URLs [26].…”

Section: Figure 4 |mentioning

confidence: 99%

Detection of Fake News on COVID-19 on Web Search Engines

2021

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In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which is misleading or even harmful, which has widely spread around the globe. Although social media are increasingly being used as the information source, web search engines, such as Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through search engines. By using a real-world dataset associated with recent COVID-19 pandemic, we first apply re-sampling techniques for class imbalance, and then we use existing machine learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated uniform resource locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URL detection, can improve the efficiency and performance of classifiers. Based on these findings, we suggest that the use of both textual and URL features can improve the effectiveness of fake news detection methods.

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Section: Figure 4 |mentioning

confidence: 99%

Detection of Fake News on COVID-19 on Web Search Engines

2021

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“…Al Asaad et al [25] proposed a news credibility verification model that combines several machine learning techniques for text classification. They tested the effectiveness of their model on a fake/real news dataset using Multinomial Naïve Bayes and Lagrangian Support Vector Machine classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

Detecting Misleading Information on COVID-19

2020

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This paper addresses the problem of detecting misleading information related to COVID-19. We propose a misleading-information detection model that relies on the World Health Organization, UNICEF, and the United Nations as sources of information, as well as epidemiological material collected from a range of fact-checking websites. Obtaining data from reliable sources should assure their validity. We use this collected ground-truth data to build a detection system that uses machine learning to identify misleading information. Ten machine learning algorithms, with seven feature extraction techniques, are used to construct a voting ensemble machine learning classifier. We perform 5-fold cross-validation to check the validity of the collected data and report the evaluation of twelve performance metrics. The evaluation results indicate the quality and validity of the collected ground-truth data and their effectiveness in constructing models to detect misleading information.

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“…Authors in [4] used TD-IDF vectorizer to extract features from news articles. Authors in [5] proposed a tool for fake news detection in which they used bag of words, bigram frequency, and TD-IDF vectorizer to extract features from news articles which were tested with probabilistic classification and linear classification. Authors in [6] analyzed different machine learning models including Naïve Bayes, Support Vector Machine (SVM), Logistic Regression and Recurrent Neural Networks (RNN) on a fake news dataset from Twitter.…”

Section: Imentioning

confidence: 99%

Analysis of Text Feature Extractors using Deep Learning on Fake News

Ahmed

Ghazanfar

Hussain

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Social media and easy internet access have allowed the instant sharing of news, ideas, and information on a global scale. However, rapid spread and instant access to information/news can also enable rumors or fake news to spread very easily and rapidly. In order to monitor and minimize the spread of fake news in the digital community, fake news detection using Natural Language Processing (NLP) has attracted significant attention. In NLP, different text feature extractors and word embeddings are used to process the text data. The aim of this paper is to analyze the performance of a fake news detection model based on neural networks using 3 feature extractors: TD-IDF vectorizer, Glove embeddings, and BERT embeddings. For the evaluation, multiple metrics, namely accuracy, precision, F1, recall, AUC ROC, and AUC PR were computed for each feature extractor. All the transformation techniques were fed to the deep learning model. It was found that BERT embeddings for text transformation delivered the best performance. TD-IDF has been performed far better than Glove and competed the BERT as well at some stages.

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A Tool for Fake News Detection

Cited by 42 publications

References 6 publications

Detection of Fake News on COVID-19 on Web Search Engines

Detection of Fake News on COVID-19 on Web Search Engines

Detecting Misleading Information on COVID-19

Analysis of Text Feature Extractors using Deep Learning on Fake News

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