A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers

Choudhury, Deepjyoti; Acharjee, Tapodhir

doi:10.1007/s11042-022-12788-1

Cited by 43 publications

(11 citation statements)

References 31 publications

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“… Authors, year of study Methods Dataset Aim Accuracy Precision Recall F1-score Random political news Buzzfeed political news LIAR FNC-1 Baseline methods Machine learning Yazdi, K.M. et al 24 , 2020 K mean clustering ✓ ✓ To improve fake news detection using ML methods – 95.34% – – Kareem, I. et al 2019 25 K-nearest neighbor To classify fake news articles on Pakistani social media 70% 70% 65% 62% Choudhury, D. 2023 26 Support vector machine ✓ To detect fake news on social network platforms using traditional ML methods 61% 62% 79% 69% Ours Random forest ✓ ✓ ✓ ✓ To develop a framework for robust and generalized fake news classification incorporated with emotes and slang words 91.1% 90.0% 91.7% ...…”

Section: Resultsmentioning

confidence: 99%

ANN: adversarial news net for robust fake news classification

Maham,

Tariq,

Khan

et al. 2024

Sci Rep

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With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

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Section: Resultsmentioning

confidence: 99%

ANN: adversarial news net for robust fake news classification

Maham,

Tariq,

Khan

et al. 2024

Sci Rep

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“…Research from Ksieniewicz et al (2019), I. Ahmad et al (2020), Hakak et al (2021), Choudhury and Acharjee (2023), Kishwar and Zafar (2023), and Ahmed and Ahmed (2023) utilized TF-IDF and BOW for text feature representation. Similarly, Reis et al (2019), Silva et al (2020), and Y.-F. Huang and Chen (2020) employed BOW as their text feature representation method.…”

Section: Data Analysis Resultsmentioning

confidence: 99%

Advancements in Rumor Detection Research Based on Bibliometrics and S-curve Technology Evolution Theory

Zhao,

Liu,

Zhang

et al. 2023

Sage Open

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With the rapid development of social media, new opportunities have been provided for the generation and dissemination of online rumors, making systematic study of rumor detection of great significance for the control and governance of internet rumors. Addressing the limitations of past review studies on rumor detection which were characterized by a single perspective, reliance on subjective judgment, and lack of technological evolution theory, this paper reviews 983 rumor detection articles in the SCI-EXPANDED, SSCI, CPCI-S, CPCI-SSH, CCR-EXPANDED, and IC databases of the Web of Science. Utilizing Citespace and VOSviewer for visual analysis of the articles, and adopting bibliometric theories like network analysis and topic evolution analysis, this study identifies core research groups in the field of rumor detection based on author collaboration network and institution collaboration network. Through high-frequency keyword clustering graph and keyword co-occurrence graph, the study unveils topic associations and cluster structures among keywords, explicates research hotspots in the field of rumor detection, and conducts a fine-grained critical comparative analysis. According to keyword time graph, keyword bursts table, and trends in the number of publications in hot technology fields, combined with the S-curve technology evolution theory, the study discerns the life cycle and research trends of technologies in the rumor detection field. Compared to existing literature reviews, this paper is the first to propose integrating bibliometrics and S-curve technology evolution theory to reveal the state of relevant technologies and research frontiers.

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“…A. ML BASED APPROACHES D. Choudhury et al [17] proposed an ML-based approach for fake news detection using three different datasets: Liar [18], Fake Job Posting [19], and Fake News. After data preprocessing, the cleaned text was then converted into numerical features using Term Frequency Inverse Document Frequency (TF-IDF) to select the categorical features, and these features were then fed to various ML-based algorithms, including Naive Bayes (NB) [20], SVM [21], LR [22], and RF [23].…”

Section: Related Workmentioning

confidence: 99%

Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI

Hashmi,

Yayilgan,

Yamin

et al. 2024

IEEE Access

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The widespread propagation of misinformation on social media platforms poses a significant concern, prompting substantial endeavors within the research community to develop robust detection solutions. Individuals often place unwavering trust in social networks, often without discerning the origins and authenticity of the information disseminated through these platforms. Hence, the identification of mediarich fake news necessitates an approach that adeptly leverages multimedia elements and effectively enhances detection accuracy. The ever-changing nature of cyberspace highlights the need for measures that may effectively resist the spread of media-rich fake news while protecting the integrity of information systems. This study introduces a robust approach for fake news detection, utilizing three publicly available datasets: WELFake, FakeNewsNet, and FakeNewsPrediction. We integrated FastText word embeddings with various Machine Learning and Deep Learning methods, further refining these algorithms with regularization and hyperparameter optimization to mitigate overfitting and promote model generalization. Notably, a hybrid model combining Convolutional Neural Networks and Long Short-Term Memory, enriched with FastText embeddings, surpassed other techniques in classification performance across all datasets, registering accuracy and F1-scores of 0.99, 0.97, and 0.99, respectively. Additionally, we utilized state-of-the-art transformerbased models such as BERT, XLNet, and RoBERTa, enhancing them through hyperparameter adjustments. These transformer models, surpassing traditional RNN-based frameworks, excel in managing syntactic nuances, thus aiding in semantic interpretation. In the concluding phase, explainable AI modeling was employed using Local Interpretable Model-Agnostic Explanations, and Latent Dirichlet Allocation to gain deeper insights into the model's decision-making process.

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A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers

Cited by 43 publications

References 31 publications

ANN: adversarial news net for robust fake news classification

ANN: adversarial news net for robust fake news classification

Advancements in Rumor Detection Research Based on Bibliometrics and S-curve Technology Evolution Theory

Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI

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