Empowering Propaganda Detection in Resource-Restraint Languages: A Transformer-Based Framework for Classifying Hindi News Articles

Chaudhari, Deptii; Pawar, Ambika Vishal

doi:10.3390/bdcc7040175

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Saleh et al (17) describe the OPCNN-false model of an optimised convolutional neural network for the detection of fake news. Using four point datasets representing fake news standards, the performance of OPCNN-FAKE is compared to that of RNN, LSTM (Long Short Term Memory), and six classical ML techniques: DT (Decision Tree), LR (Logistic Regression), KNN (K Nearest Neighbour), RF (Random Forest), SVM, and NB.…”

Section: Related Workmentioning

confidence: 99%

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

Maheswari,

Sudha

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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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

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Section: Related Workmentioning

confidence: 99%

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

Maheswari,

Sudha

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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“…Retraining helps BERT to attune to the most current information, such that its predictions and interpretations are contemporary and relevant [38]. This continuous adaptation is fundamental in sectors where the timeliness and currency of the information are essential [39].…”

Section: Retraining the Bert Modelmentioning

confidence: 99%

Computers’ Interpretations of Knowledge Representation Using Pre-Conceptual Schemas: An Approach Based on the BERT and Llama 2-Chat Models

Insuasti,

Roa,

Zapata-Jaramillo

2023

BDCC

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Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose an approach to making computers able to interpret the basic pre-conceptual schemas made by humans. To do that, the construction of a linguistic corpus is required to work with large language models—LLM. The linguistic corpus was mainly fed using Master’s and doctoral theses from the digital repository of the University of Nariño to produce a training dataset for re-training the BERT model; in addition, we complement this by explaining the elicited sentences in triads from the pre-conceptual schemas using one of the cutting-edge large language models in natural language processing: Llama 2-Chat by Meta AI. The diverse topics covered in these theses allowed us to expand the spectrum of linguistic use in the BERT model and empower the generative capabilities using the fine-tuned Llama 2-Chat model and the proposed solution. As a result, the first version of a computational solution was built to consume the language models based on BERT and Llama 2-Chat and thus automatically interpret pre-conceptual schemas by computers via natural language processing, adding, at the same time, generative capabilities. The validation of the computational solution was performed in two phases: the first one for detecting sentences and interacting with pre-conceptual schemas with students in the Formal Languages and Automata Theory course—the seventh semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. The second phase was for exploring the generative capabilities based on pre-conceptual schemas; this second phase was performed with students in the Object-oriented Design course—the second semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. This validation yielded favorable results in implementing natural language processing using the BERT and Llama 2-Chat models. In this way, some bases were laid for future developments related to this research topic.

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Pandey,

Nazarov

2023

Int. j. inf. tecnol.

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Empowering Propaganda Detection in Resource-Restraint Languages: A Transformer-Based Framework for Classifying Hindi News Articles

Cited by 4 publications

References 36 publications

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

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

Computers’ Interpretations of Knowledge Representation Using Pre-Conceptual Schemas: An Approach Based on the BERT and Llama 2-Chat Models

Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

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