Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review

Capuano, Nicola; Fenza, Giuseppe; Loia, Vincenzo; Nota, Francesco David

doi:10.1016/j.neucom.2023.02.005

Cited by 45 publications

(18 citation statements)

References 34 publications

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“…Rani et al (2022) explored the methods to improve users’ trust in the identification results through psychological intervention and social network analysis from the perspective of psychology and sociology. Capuano et al (2023) sorted out the content- and context-based rumor identification methods from the perspective of computer science and data science, comparing the accuracy of rumor identification under different types of methods. Athira et al (2023) discussed the goals, methods, and challenges of explainable rumor detection, and the methods to improve users’ understanding of identification results from the perspective of interpretability.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Sentiment analysis primarily involves text classification and determination of text polarity, which can be binary (positive-negative) or ternary (positive, negative, and neutral) [5], or even more granular. For instance, sentiment analysis has been employed for fake news detection [6], where data labels extracted from social media are classified into seven categories. In a similar vein, a separate fake news detector [7] utilized the Mahalanobis distance method to identify outliers in an unlabeled dataset due to the lack of a labeled dataset in the model's training phase.…”

Section: Introductionmentioning

confidence: 99%

Persian Sentiment Analysis via a Transformer Model concerning Banking Sector

Haddadi,

Khoeini,

Salmani

et al. 2023

Preprint

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The competitive landscape of a country's banking sector necessitates an in-depth understanding of customer satisfaction levels concerning the services provided. Presently, customers predominantly express their feedback via social media platforms in the form of posts and comments. This study endeavors to create a highly accurate sentiment detection algorithm for the Iranian banking system, utilizing a transformer model. In the initial stages, we collected data by crawling comments from Twitter, which are subsequently labeled and filtered according to the names of Iranian banks, dating from 2019. Following this, an optimized Deep Neural Network (DNN)-based pre-trained ParsBERT model, a monolingual Persian model, is fine-tuned using this data. Finally, our model is evaluated on a test dataset, and the results are validated by comparing them with the original multilingual BERT, Bidirectional Long Short-Term Memory (Bi-LSTM) network, and four other classification methods. To address the Out-Of-Vocabulary (OOV) issue, a character-level embedding is incorporated in conjunction with the word-level embedding. This approach aids in tackling the multitude of variations observed in non-native words, extracting character-level features using a character-level Bi-LSTM. The proposed model highlights the statistical superiority of our method when compared to the other methods evaluated.

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“…Entre os trabalhos baseados em conteúdo, há métodos que analisam características linguísticas, que sejam informativas para diferenciar notícias verdadeiras de falsas. No entanto, indícios de notícias falsas diferem entre tópicos, linguagens e domínios, demandando adaptações dinâmicas em estratégias anteriormente propostas (CAPUANO et al, 2023;SHARMA et al, 2019;BONDIELLI;MARCELLONI, 2019;ZHOU et al, 2019).…”

Section: Motivação E Lacunasunclassified

“…Diversos algoritmos de extração de padrões foram propostos nos últimos anos para a tarefa de detecção de notícias falsas. As abordagens abrangem algoritmos supervisionados, semissupervisionados e não supervisionados, cujas notícias são representadas de forma estruturada considerando modelos de representação espaço-vetoriais ou estruturas de redes (CAPUANO et al, 2023;AÏMEUR;AMRI;BRASSARD, 2023;SHAHID et al, 2022;FONTES;JÚNIOR, 2020;GHORBANI, 2020;BONDIELLI;MARCELLONI, 2019;MEEL;VISHWAKARMA, 2019;SHARMA et al, 2019).…”

Section: Extração De Padrões Para Detecção De Notícias Falsasunclassified

“…Modelos de aprendizado baseados em redes neurais também foram amplamente propostos, usando técnicas como Multi-Layer Perceptron (MLP), Deep Neural Networks, Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM) e Transformers para detecção de notícias falsas (CAPUANO et al, 2023;KALIYAR et al, 2020;KANG;HWANG;QAZI;ALI, 2020;WANG et al, 2020;WU et al, 2020a;YAVARY;SAJEDI;ABADEH, 2020;BONDIELLI;MARCELLONI, 2019;MEEL;VISHWAKARMA, 2019;SHARMA et al, 2019;DONG et al, 2019;MONTI et al, 2019;PHILIP, 2019;LI et al, 2019).…”

Section: Algoritmos De Aprendizado Baseados No Modelo Espaço-vetorialunclassified

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Detecção de notícias falsas usando poucos dados positivos rotulados

Souza

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Agradeço a Deus, que sempre me deu coragem e bom ânimo para seguir em frente. Agradeço aos meus pais, Jorge e Marli, minha base sólida, por me ensinarem tantos valores e me apoiarem em todas as decisões importantes. Cada conquista ao longo desta jornada é reflexo da dedicação e carinho que sempre recebi de vocês. Agradeço ao meu irmão, Gabriel, que morou comigo parte do período de doutorado, sendo um apoio em dias difíceis. Agradeço também ao meu parceiro, Thiago, que segurou minha mão em todo o percurso, e mesmo com a distância, sempre me incentivou a cumprir meus objetivos.

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Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review

Cited by 45 publications

References 34 publications

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

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

Persian Sentiment Analysis via a Transformer Model concerning Banking Sector

Detecção de notícias falsas usando poucos dados positivos rotulados

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