Fake News Detection Using Machine Learning and Deep Learning Algorithms

Abdulrahman, Awf; Baykara, Muhammet

doi:10.1109/icoase51841.2020.9436605

Cited by 28 publications

(14 citation statements)

References 7 publications

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“…This part converts the text data to 0 and 1 vectors. In addition, this method converts the text file to new vectors [16]. Three vectorizing techniques were used for the machine learning part: TF-IDF, the N-Gram level vectorizer, the Count vectorizer (CV)), and the tokenizer with an embedding layer for deep learning.…”

Section: B Vectorizing Methodsmentioning

confidence: 99%

“…Where [16] 𝑡𝑓 𝑑 𝑖 : represents how many times i appears in the document d. 𝑁 : represents number of the total documents in the document set. 𝑑𝑓 𝑖 : these are the documents in which the term i is occurring.…”

Section: ) Tf-idf Vectorizermentioning

confidence: 99%

“…This method consists of a slice (N) of letters representing the degrees of TF-IDF. This method was first proposed to overcome the problem of selecting the correct features and their numerical value by using the TF-IDF classification associated with unigrams or bigrams [16] [18].…”

Section: ) N-gram Level Vectorizermentioning

confidence: 99%

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A New Approach to Detect Fake News Related to Covid-19 Pandemic Using Deep Neural Network

Qdroo

Baykara

2022

JASTT

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The fake news that accompanied the COVID-19 pandemic on social media platforms negatively affected people and led to a state of panic and fear of the unknown. This study aims to build a model for classifying textual news for four datasets related to COVID-19, binary classification (fake and real) with high performance. Two hybrid deep learning models were built. The first model consists of three layers of a one-dimension convolutional neural network (1D-CNN), followed by two layers of a long-short-term memory neural network (LSTM). The second model consists of three layers of a 1D-CNN followed by two layers of a bidirectional LSTM neural network (BiLSTM). Finally, the results obtained using hybrid models were compared with the results obtained by applying three machine learning classifiers (naïve Bayes, logistic regression, and k-nearest neighbor) on the same data sets. This study achieved promising results with an accuracy of (96.98%, 94.52%, 99.60%, and 99.90%) for the first model with all data sets and (97.15%, 95.32%, 99.40%, and 99.82%) for the second model with the same four data sets.

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Section: B Vectorizing Methodsmentioning

confidence: 99%

Section: ) Tf-idf Vectorizermentioning

confidence: 99%

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A New Approach to Detect Fake News Related to Covid-19 Pandemic Using Deep Neural Network

Qdroo

Baykara

2022

JASTT

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“…In (2), a comparative study of various machine learning methods and linguistically motivated features used in classifying exam questions based on BT cognitive levels automatically is presented. Through the experiment conducted, the average accuracy result of above 0.6 obtained by four classifiers which are SVM, Logistic regression, decision trees and NB using the unigram feature concluded that using machine learning models in question classification can achieve a high level of accuracy.…”

Section: Related Work In Exam Question Classificationmentioning

confidence: 99%

“…Figure 3: Proposed Question Classification Model here n t q is the number of occurrences of term t in a question q, k n k q is total occurrences of all terms in a question q, this variant used in these past researches [2,47].…”

Section: Variants Of Term Weightingmentioning

confidence: 99%

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

Sangodiah

San

Fui

et al. 2022

mendel

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Examination is one of the common ways to evaluate the students’ cognitive levels in higher education institutions. Exam questions are labeled manually by educators in accordance with Bloom’s taxonomy cognitive domain. To ease the burden of the educators, several past research works have proposed the automated question classification based on Bloom’s taxonomy using the machine learning technique. Feature selection, feature extraction and term weighting are common ways to improve the accuracy of question classification. Commonly used term weighting method in the past work is unsupervised namely TF and TF-IDF. There are several variants of TF and TFIDF and the most optimal variant has yet to be identified in the context of question classification based on BT. Therefore, this paper aims to study the TF, TF-IDF and normalized TF-IDF variants and identify the optimal variant that can enhance the exam question classification accuracy. To investigate the variants two different classifiers were used, which are Support Vector Machine (SVM) and Naïve Bayes. The average accuracies achieved by TF-IDF and normalized TF-IDF variants using SVM classifier were 64.3% and 72.4% respectively, while using Naïve Bayes classifier the average accuracies for TF-IDF and normalized TF-IDF were 61.9% and 63.0% respectively. Generally, the normalized TF-IDF variants outperformed TF and TF-IDF variants in accuracy and F1-measure respectively. Further statistical analysis using t-test and Wilcoxon Signed also shows that the differences in accuracy between normalized TF-IDF and TF, TF-IDF are significant. The findings from this study show that the Normalized TF-IDF3 variant recorded the highest accuracy of 74.0% among normalized TF-IDF variants. Also, the differences in accuracy between Normalized TF-IDF3 and other normalized variants are generally significant, thus the optimal variant is Normalized TF-IDF3. Therefore, the normalized TF-IDF3 variant is important for benchmarking purposes, which can be used to compare with other term weighting techniques in future work.

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Browser Extension for Detection of Fake News and Disinformation

Hasimi

Poniszewska-Marańda

2023

Lecture Notes in Business Information Processing

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Fake News Detection Using Machine Learning and Deep Learning Algorithms

Cited by 28 publications

References 7 publications

A New Approach to Detect Fake News Related to Covid-19 Pandemic Using Deep Neural Network

A New Approach to Detect Fake News Related to Covid-19 Pandemic Using Deep Neural Network

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

Browser Extension for Detection of Fake News and Disinformation

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