Improving Prediction of Arabic Fake News Using Fuzzy Logic and Modified Random Forest Model

Wotaifi, Tahseen A.; Dhannoon, Ban N.

doi:10.33640/2405-609x.3241

Cited by 7 publications

(9 citation statements)

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“…The embedding process translates semantic meaning into geometric meaning, Word2Vec [8,23,25], Global vectors (GloVe) for word representation [18], n-grams [11,26], word-embedding [12], BOW [7] are the pioneers of word representativeness approaches. Classification Step: Different methods of machine learning and deep learning are used by different researchers, and machine learning algorithms, such as SVM [3,4,7,9,11,14,15,18,21,24,26,[37][38][39], Decision tree [1,4,13,26], logistic regression [15,20,25], Naïve Bayes [9,40], random forest [25,41,42], XGBoost [25]) were applied. Recently, deep learning methods are used like CNN [12,18,43], a hybrid of CNN-LSTM [25], and LRCN [12].…”

Section: Methodsmentioning

confidence: 99%

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Haider Ameen,

Saad Al-Mukhtar,

Qahtan Ahmed

et al. 2024

ANJS

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Cyberbullying is a new form of online violence that has emerged as a result of the social media industry's explosive expansion since it allows for indirect communication. Despite the usefulness of digital media, it has been used by weak people to threaten and bully others online. In the last ten years, research has shown that children and teenagers are increasingly experiencing cyberbullying as a concern. This paper examines the research conducted during the previous ten years, categorizes it, and presents statistical analyses of the data collected during that time. A table is used to present various data, including the dataset that was used, its size (number of samples, posts, or messages), the methods that were employed, and the metrics that were gathered from the examined research that was taken from the publications that were looked into. This survey will be helpful to everyone who wants to advance their understanding of how machine learning may be used to identify cyberbullying, and it may help create a social media environment that is safe and relatively healthy by automatically identifying bullying communications.

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Section: Methodsmentioning

confidence: 99%

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Haider Ameen,

Saad Al-Mukhtar,

Qahtan Ahmed

et al. 2024

ANJS

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“…Tokenization describes interpreting and grouping isolated tokens to generate higher-level tokens in addition to dividing strings into fundamental processing units. Word tokenization includes preprocessed raw texts are divided into textual units 18,19 . During the dataset cleaning stage, the columns from the datasets that weren't needed for processing were removed.…”

Section: Data Pre-processingmentioning

confidence: 99%

Fake News Detection Model Basing on Machine Learning Algorithms

Taha,

Jabar,

Mohammed

2024

Baghdad Sci.J

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The rapid growth of the internet and easy communication has made it quick and simple to create and spread news. Social media users now generate and share more information than before, but some of it is false and unrelated to reality. Detecting false information in text is challenging, even for experts who need to consider multiple factors to determine authenticity. Malicious misinformation on social media negatively affects societies, especially during crises like terrorist attacks, riots, and natural disasters. To minimize the harmful impact, it is crucial to identify rumors quickly. This study aims to build a learning model for detecting fake news. This research paper relies on finding and analyzing the characteristics of the text, then the words are converted into features using TF-IDF technology, after that the highest-ranking features are identified for the purpose of studying and distinguishing the spread of news, whether it is real or fake using machine learning techniques. Finally, the Logistic Regression, Decision Tree, Gradient Boosting and Random Forest algorithm has been adapted. The accuracy of Logistic Regression is 0.985, Random Forest (0.989) whereas the accuracy of Decision Tree is 0.994 and Gradient Boosting (0.9949), respectively.

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“…In eq. ( 2), TF (w) signifies the frequency of the word w in the document, count (w) and count (wn) denote the number of samples including the word w in the dataset, and n denotes the number of samples containing the word w in a corpus, respectively [29]. IDF (w) denotes the inverse file frequency of the word w in equation (3).…”

Section: Feature Extractionmentioning

confidence: 99%

Comparing the Random Forest vs. Extreme Gradient Boosting using Cuckoo Search Optimizer for Detecting Arabic Cyberbullying

Saadi,

Dhannoon

2023

Iraqi Journal of Science

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Cyberbullying is one of the major electronic problems, and it is not a new phenomenon. It was present in the traditional form before the emergence of social networks, and cyberbullying has many consequences, including emotional and physiological states such as depression and anxiety. Given the prevalence of this phenomenon and the importance of the topic in society and its negative impact on all age groups, especially adolescents, this work aims to build a model that detects cyberbullying in the comments on social media (Twitter) written in the Arabic language using Extreme Gradient Boosting (XGBoost) and Random Forest methods in building the models. After a series of pre-processing, we found that the accuracy of classification of these comments was 0.861 in XGBoost, and 0.849 in Random Forest. Then the results of this model were improved by using one of the optimization algorithms called cuckoo search to adjust the parameters in two methods. The results are improved clearly in the random forest method, which obtained results similar to the extreme gradient boosting method, with a value of 0.867.

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Improving Prediction of Arabic Fake News Using Fuzzy Logic and Modified Random Forest Model

Cited by 7 publications

References 0 publications

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Fake News Detection Model Basing on Machine Learning Algorithms

Comparing the Random Forest vs. Extreme Gradient Boosting using Cuckoo Search Optimizer for Detecting Arabic Cyberbullying

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