CMGN: Text GNN and RWKV MLP-mixer combined with cross-feature fusion for fake news detection

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129811 - 129811

Опубликована: Март 1, 2025

Язык: Английский

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A systematic review of multimodal fake news detection on social media using deep learning models

Results in Engineering, Год журнала: 2025, Номер 26, С. 104752 - 104752

Опубликована: Апрель 9, 2025

Язык: Английский

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A reliable fake news detection model using a hybrid convolution (1D-2D)-based adaptive temporal convolutional network with feature extraction

Intelligent Decision Technologies, Год журнала: 2025, Номер unknown

Опубликована: Апрель 22, 2025

News is considered the essential element in an individual's day-to-day life to update their knowledge of world. Yet, news websites are circulating minimal quality with more misinformation, and this can easily spread as fake among individuals. Fake Detection (FND) framework has been introduced recently curb transmission on social media, websites, but system requires manual intervention. it a rigorous task manually identify large media platforms. Hence, necessary through advanced automated deep learning models. The several sources provide multimedia data volumes. Thus, novel FND model by proposed work automatically from real information. At first, containing various text image files collected benchmark dataset sites. For extracting features data, Text Convolutional Neural Networks (TextCNN) method used, identifies text-related feature set 1. directly given for represented 2. Next, Hybrid Convolution (1D-2D)-based Adaptive Temporal Network (HC-TCN) processing high-resolution 2, HC-TCN processes both information define class related original news. improving effectiveness accuracy detection, improved Improved Lotus Effect Optimization Algorithm (ILEA). final detection results implemented compared other techniques justify its robustness detection.

Язык: Английский

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Efficient control of spider-like medical robots with capsule neural networks and modified spring search algorithm

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 22, 2025

Язык: Английский

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Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society

PLoS ONE, Год журнала: 2024, Номер 19(12), С. e0315407 - e0315407

Опубликована: Дек. 19, 2024

Informal education via social media plays a crucial role in modern learning, offering self-directed and community-driven opportunities to gain knowledge, skills, attitudes beyond traditional educational settings. These platforms provide access broad range of learning materials, such as tutorials, blogs, forums, interactive content, making more accessible tailored individual interests needs. However, challenges like information overload the spread misinformation highlight importance digital literacy ensuring users can critically evaluate credibility information. Consequently, significance sentiment analysis has grown contemporary times due widespread utilization means for individuals articulate their viewpoints. Twitter (now X) is well recognized prominent platform that predominantly utilized microblogging. Individuals commonly engage expressing viewpoints regarding events, hence presenting significant difficulty scholars categorize associated with expressions effectively. This research study introduces highly effective technique detecting related COVID-19 pandemic. The fake news during pandemic created public health safety because about virus, its transmission, treatments led confusion distrust among public. introduce techniques methodology this work includes gathering dataset comprising fabricated articles sourced from corpus subjected natural language processing (NLP) cycle. After applying some filters, total five machine classifiers three deep were employed forecast articles, distinguishing between those are authentic fabricated. employs classifiers, namely Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, analyze compare obtained results. Convolutional Neural Networks, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) afterwards compares results indicate BiGRU classifier demonstrates high accuracy efficiency, following indicators: 0.91, precision 0.90, recall 0.93, F1-score 0.92. For same algorithm, true negatives, positives came out be 555 580, respectively, whereas, false negatives 81, 68, respectively. In conclusion , highlights effectiveness COVID-19, emphasizing fostering resilience against society. implications higher lifelong learners it potential using advanced help educators institutions process combating promoting critical thinking skills students. By these methods classify develop tools curricula teaching validation, equipping students needed discern context beyond. extrapolate creation society resistant through platforms.

Язык: Английский

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Comparative Evaluation of Network-Based and NLP-based Methods for Detecting Conspiracy Theory Communities on YouTube

2021 IEEE International Conference on Big Data (Big Data), Год журнала: 2024, Номер unknown, С. 3042 - 3050

Опубликована: Дек. 15, 2024

Язык: Английский

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