Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

Computational and Mathematical Methods in Medicine, Год журнала: 2021, Номер 2021, С. 1 - 14

Опубликована: Ноя. 15, 2021

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and networking. Twitter an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these the primary concern Sentiment analysis or opinion mining. Efficiently capturing, gathering, sentiments have been challenging researchers. To deal with challenges, in this research work, we propose a highly accurate approach SA fake news on COVID-19. The dataset contains COVID-19; started by preprocessing (replace missing value, noise removal, tokenization, stemming). We applied semantic model term frequency inverse document weighting representation. In measuring evaluation step, eight machine-learning algorithms such as Naive Bayesian, Adaboost, $K$ -nearest neighbors, random forest, logistic regression, decision tree, neural networks, support vector machine four deep learning CNN, LSTM, RNN, GRU. Afterward, based results, boiled efficient prediction python, trained evaluated classification according to performance measures (confusion matrix, rate, true positives rate...), then tested set unclassified COVID-19, predict sentiment class each Obtained results demonstrate high accuracy compared other models. Finally, recommendations provided future directions help researchers select data.

Язык: Английский

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Detection and moderation of detrimental content on social media platforms: current status and future directions

Social Network Analysis and Mining, Год журнала: 2022, Номер 12(1)

Опубликована: Сен. 5, 2022

Язык: Английский

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Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

International Journal of Environmental Research and Public Health, Год журнала: 2021, Номер 18(16), С. 8578 - 8578

Опубликована: Авг. 13, 2021

The COVID-19 pandemic has wreaked havoc in every country the world, with serious health-related, economic, and social consequences. Since its outbreak March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, support for this work artificial intelligence (AI) other emerging concepts linked intelligent data analysis been decisive. enormous amount research high number publications during period makes it difficult obtain an overall view applications AI management understanding how field evolving. Therefore, paper, we carry out scientometric area supported by text mining, including review 18,955 related Scopus database 2020 June 2021 inclusive. For purpose, used VOSviewer software, which was developed at Leiden University Netherlands. This allowed us examine exponential growth on issue distribution country, highlight clear hegemony United States (USA) China respect. We automatic process extract topics interest observed that most important current lines focused patient-based solutions. also identified relevant journals terms pandemic, demonstrated growing value open-access publication, highlighted influential authors means citations co-citations. study provides overview status application pandemic.

Язык: Английский

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A review on fake news detection 3T’s: typology, time of detection, taxonomies

International Journal of Information Security, Год журнала: 2022, Номер 22(1), С. 177 - 212

Опубликована: Ноя. 15, 2022

Язык: Английский

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OptNet-Fake: Fake News Detection in Socio-Cyber Platforms Using Grasshopper Optimization and Deep Neural Network

IEEE Transactions on Computational Social Systems, Год журнала: 2023, Номер 11(4), С. 4965 - 4974

Опубликована: Март 3, 2023

Exposure to half-truths or lies has the potential undermine democracies, polarize public opinion, and promote violent extremism. Identifying veracity of fake news is a challenging task in distributed disparate cyber-socio platforms. To enhance trustworthiness on these platforms, this article, we put forward detection model, OptNet-Fake. The proposed model architecturally hybrid that uses meta-heuristic algorithm select features based usefulness trains deep neural network detect social media. $d$ -D feature vectors for textual data are initially extracted using term frequency inverse document (TF-IDF) weighting technique. then directed modified grasshopper optimization (MGO) algorithm, which selects most salient text. selected fed various convolutional networks (CNNs) with different filter sizes process them obtain notation="LaTeX">$n$ -gram from These finally concatenated news. results evaluated four real-world datasets standard evaluation metrics. A comparison algorithms recent methods also done. distinctly endorse superior performance OptNet-Fake over contemporary models across datasets.

Язык: Английский

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Fake news detection: Taxonomy and comparative study

Information Fusion, Год журнала: 2023, Номер 103, С. 102140 - 102140

Опубликована: Ноя. 14, 2023

Язык: Английский

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Fake News in Virtual Community, Virtual Society, and Metaverse: A Survey

IEEE Transactions on Computational Social Systems, Год журнала: 2023, Номер 11(4), С. 4828 - 4842

Опубликована: Фев. 6, 2023

In the trend of accelerated progression communication network technology, emergence virtual communities (VCs), societies (VSs), metaverse, and other technologies not only makes data access sharing easier but also leads to proliferation fake news (FN). To effectively monitor identify FN in VC, VS, create a safer space, this work takes metaverse as objects. First, content display methods are reviewed explained, it is understood that mainly displayed by single-modal multimodal representations. Second, application scenarios many important fields such transportation analyzed, so further understand impact detection effect different scenarios. Finally, an intelligent outlook summary analysis carried out on information security FN, which provides theoretical reference new opportunities for identification cyberspace.

Язык: Английский

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Combating the menace: A survey on characterization and detection of fake news from a data science perspective

International Journal of Information Management Data Insights, Год журнала: 2021, Номер 1(2), С. 100052 - 100052

Опубликована: Ноя. 1, 2021

Journalism has always remained a vital constituent of our society and journalists play key role in making people aware the happenings developments society. This spread information enables shaping ideologies, orientations thoughts individuals as well Contrary to this, misinformation or fake news leads detrimental consequences. With advent social media, menace become grievous due unrestrained propagation difficulty track several accounts operated by humans bots. can be mitigated through data science approaches combining artificial intelligence with statistics domain-based knowledge. In this paper, survey works aimed at characterization, feature extraction subsequent detection been conducted from perspective. Along it, an analysis 8 renowned repositories presented. Furthermore, case study on tweets related COVID-19 pandemic, factors behind during critical times, distinguishing between factual emotional viable restrain enunciated.

Язык: Английский

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Review on the COVID-19 pandemic prevention and control system based on AI

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 114, С. 105184 - 105184

Опубликована: Июль 11, 2022

Язык: Английский

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DC-CNN: Dual-channel Convolutional Neural Networks with attention-pooling for fake news detection

Applied Intelligence, Год журнала: 2022, Номер 53(7), С. 8354 - 8369

Опубликована: Авг. 1, 2022

Язык: Английский

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