Machine Learning and Deep Learning Applications in Disinformation Detection: A Bibliometric Assessment

Electronics, Год журнала: 2024, Номер 13(22), С. 4352 - 4352

Опубликована: Ноя. 6, 2024

Fake news is one of the biggest challenging issues in today’s technological world and has a huge impact on population’s decision-making way thinking. Disinformation can be classified as subdivision fake news, main purpose which to manipulate generate confusion among people order influence their opinion obtain certain advantages multiple domains (politics, economics, etc.). Propaganda, rumors, conspiracy theories are just few examples common disinformation. Therefore, there an urgent need understand this phenomenon offer scientific community paper that provides comprehensive examination existing literature, lay foundation for future research areas, contribute fight against The present manuscript detailed bibliometric analysis articles oriented towards disinformation detection, involving high-performance machine learning deep algorithms. dataset been collected from popular Web Science database, through use specific keywords such “disinformation”, “machine learning”, or “deep followed by manual check papers included dataset. documents were examined using R tool, Biblioshiny 4.2.0; perspectives various facets: overview, sources, authors, papers, n-gram analysis, mixed analysis. results highlight increased interest topics context learning, supported annual growth rate 96.1%. insights gained bring light surprising details, while study solid basis both area, well development new strategies addressing complex issue ensuring trustworthy safe online environment.

Язык: Английский

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A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Язык: Английский

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Content Analysis Using Specific Natural Language Processing Methods for Big Data

Electronics, Год журнала: 2024, Номер 13(3), С. 584 - 584

Опубликована: Янв. 31, 2024

Researchers from different fields have studied the effects of COVID-19 pandemic and published their results in peer-reviewed journals indexed international databases such as Web Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating extensive literature research, our study conducts a content analysis top 1000 cited papers WoS that delve into subject by using elements natural language processing (NLP). Knowing WoS, scientific paper is described group Paper = {Abstract, Keyword, Title}; we obtained via NLP word dictionaries with frequencies use cloud 100 most used words, investigated if there degree similarity between titles abstracts, respectively. Using Python packages NLTK, TextBlob, VADER, computed sentiment scores analyzed results, then, Azure Machine Learning-Sentiment analysis, extended range comparison scores. Our proposed method can be applied to any research topic or theme papers, articles, projects various specialization create minimal dictionary terms based frequency use, visual representation cloud. Complementing highlights similar treatment topics addressed well opinions feelings conveyed authors relation researched issue.

Язык: Английский

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

SN Computer Science, Год журнала: 2024, Номер 5(5)

Опубликована: Май 16, 2024

Язык: Английский

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