Enhancing Fake News Detection by Multi-Feature Classification

Almarashy, Ahmed Hashim Jawad; Feizi-Derakhshi, Mohammad-Reza; Salehpour, Pedram

doi:10.1109/access.2023.3339621

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aditya and Mohanty [32] presented an approach for heterogeneous social media analysis for efficient deep learning fake-profile identification, demonstrating the necessity of addressing diverse data types in social media. Almarashy et al [33] enhanced fake news detection through a multi-feature classification, showcasing the importance of incorporating multiple data features for accurate detection. Kar et al [34] addressed the challenge of detecting fake images on social networks using recurrent neural networks, indicating the increasing complexity of fake news formats.…”

Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In an era where social media platforms burgeon with diverse content, effective content moderation becomes imperative to filter harmful materials. Traditional methods often grapple with the dual challenges of accuracy and computational efficiency levels. These conventional approaches typically rely on either text-based or image-based analysis, neglecting the complex interplay of multimodal content prevalent in social media scenarios. This limitation leads to suboptimal content filtering, often missing contextually nuanced or visually deceptive harmful content sets. Addressing these challenges, the proposed work introduces an innovative, hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Transformers for nuanced visual content extraction from social media posts. CNNs adeptly handle image features, while Transformers enrich the analysis with contextual textual understanding, thereby enhancing accuracy in identifying harmful contents. Additionally, a Bi-directional Attention Mechanism (BAM) is employed for efficient text-visual fusion, dynamically prioritizing relevant information and reducing computational loads. The fusion process is further refined using Genetic Algorithms (GAs) for hyperparameter optimization, streamlining the model's performance levels. The model's robustness is demonstrated through its ability to discern complex intra and inter-modal relationships within social media content, facilitated by Graph Neural Networks (GNNs). Moreover, the model's scalability and deployment efficiency on social media platforms are ensured through quantization and pruning techniques, balancing accuracy with computational practicality levels. Empirical testing on datasets from Google, Facebook, and Kaggle reveals the model's superior performance over existing methods, with notable improvements in precision, accuracy, recall, AUC, specificity, and response delay in detecting harmful contents. The model also excels in preempting potential harmful content posters, offering enhanced pre-emption metrics.

show abstract

Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…(4,5) The extensive use of fake news leading up to the 2016 US presidential election is thought to be a contentious subject that influences public opinion. (6) The propagation of false information on social media at an accelerated rate significantly raises the possibility of a catastrophic effect. As a result, the transmission of false information is a worldwide issue, and several nations have made it illegal to produce and disseminate false information online.…”

Section: Introductionmentioning

confidence: 99%

An efficient fake news classification model based on ensemble deep learning techniques

Maheswari,

Sudha

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

View full text Add to dashboard Cite

The availability and expansion of social media has made it difficult to distinguish between fake and real news. Information falsification has exponentially increased as a result of how simple it is to spread information through sharing. Social media dependability is also under jeopardy due to the extensive dissemination of false information. Therefore, it has become a research problem to automatically validate information, specifically source, content, and publisher, to identify it as true or false. Despite its limitations, machine learning (ML) has been crucial in the categorization of information. Previous studies suggested three-step methods for categorising false information on social media. In the first step of the process, the data set is subjected to a number of pre-processing processes in order to transform unstructured data sets into structured data sets. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by WOA (Whale Optimization Algorithm) for weight value to tune the classification part. Finally, a Hybrid Classification model that is hybrid with a fuzzy based Convolutional Neural Network and kernel based support vector machine is constructed in order to identify the data pertaining to bogus news. However using single classifier for fake news detection produces the insufficient accuracy. To overcome this issue in this work introduced an improved model for fake news classification. To turn unstructured data sets into structured data sets, a variety of pre-processing operations are used on the data set in the initial phase of the procedure. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by COA (Coati Optimization Algorithm) for weight value to tune the classification part. Finally, an ensemble of RNN (Recurrent Neural Networks), VGG-16 and ResNet50.A classification model was developed to recognise bogus news information. Evaluate each fake news analysis' performance in terms of accuracy, precision, recall, and F1 score. The suggested model, out of all the methodologies taken into consideration in this study, provides the highest outcomes, according to experimental findings

show abstract

Elevating Fake News Detection Through Deep Neural Networks, Encoding Fused Multi-Modal Features

Hashim Jawad Almarashy,

Feizi-Derakhshi,

Salehpour

2024

IEEE Access

View full text Add to dashboard Cite

Enhancing Fake News Detection by Multi-Feature Classification

Cited by 5 publications

References 32 publications

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

An efficient fake news classification model based on ensemble deep learning techniques

Elevating Fake News Detection Through Deep Neural Networks, Encoding Fused Multi-Modal Features

Contact Info

Product

Resources

About