Deep Learning for Combating Misinformation in Multicategorical Text Contents

Kozik, Rafał; Mazurczyk, Wojciech; Cabaj, Krzysztof; Pawlicka, Aleksandra; Pawlicki, Marek; Choraś, Michał

doi:10.3390/s23249666

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Understanding the causes and motivations behind disinformation has been another key area of interest [13][14][15]. These approaches, which emanate from various academic disciplines, seek to illuminate the intricate dynamics of disinformation, thus promoting the development of more effective strategies to mitigate the adverse effects that this phenomenology generates in the social fabric [16,17].…”

Section: Introductionmentioning

confidence: 99%

Research on Disinformation in Academic Studies: Perspectives through a Bibliometric Analysis

Navarro-Sierra,

Magro-Vela,

Vinader-Segura

2024

Publications

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Disinformation is a phenomenon of concern to all political systems, as it poses a threat to freedom and democracy through the manipulation of public opinion aimed at eroding institutions. This paper presents a bibliometric and systematized study which allows the establishment of a comprehensive view of the research and current state of academic investigations on disinformation. To this end, a content analysis of the scientific articles indexed in Scopus up to 31 December 2023 has been carried out based on three categories of analysis: journals, authors and investigations. Similarly, a systematic study of the 50 most cited articles in this sample was performed in order to gain a deeper understanding of the nature, motivations and methodological approaches of these investigations. The results indicate that disinformation is a research topic which has gained great interest in the academic community since 2018, with special mention to the impact of COVID-19 and the vaccines against this disease. Thus, it can be concluded that disinformation is an object of study which attracts significant attention and which must be approached from transdisciplinarity to respond to a phenomenon of great complexity.

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Section: Introductionmentioning

confidence: 99%

Research on Disinformation in Academic Studies: Perspectives through a Bibliometric Analysis

Navarro-Sierra,

Magro-Vela,

Vinader-Segura

2024

Publications

View full text Add to dashboard Cite

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“…Many researchers have primarily focused on using Natural Language Processing (NLP) techniques to detect fake text-based content [21][22][23][24], often overlooking the fact that news articles frequently include both textual and visual elements. An illustrative example can be seen in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Learning for Misinformation Detection Based on Contrastive Models

Zheng,

Chen,

et al. 2024

Electronics

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With the development of social media, the amount of fake news has risen significantly and had a great impact on both individuals and society. The restrictions imposed by censors make the objective reporting of news difficult. Most studies use supervised methods, relying on a large amount of labeled data for fake news detection, which hinders the effectiveness of the detection. Meanwhile, the focus of these studies is on the detection of fake news in a single modality, either text or images, but actual fake news is more often in the form of text–image pairs. In this paper, we introduce a self-supervised model grounded in contrastive learning. This model facilitates simultaneous feature extraction for both text and images by employing dot product graphic matching. Through contrastive learning, it augments the extraction capability of image features, leading to a robust visual feature extraction ability with reduced training data requirements. The model’s effectiveness was assessed against the baseline using the COSMOS fake news dataset. The experiments reveal that, when detecting fake news with mismatched text–image pairs, only approximately 3% of the data are used for training. The model achieves an accuracy of 80%, equivalent to 95% of the original model’s performance using full-size data for training. Notably, replacing the text encoding layer enhances experimental stability, providing a substantial advantage over the original model, specifically on the COSMOS dataset.

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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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Deep Learning for Combating Misinformation in Multicategorical Text Contents

Cited by 3 publications

References 43 publications

Research on Disinformation in Academic Studies: Perspectives through a Bibliometric Analysis

Research on Disinformation in Academic Studies: Perspectives through a Bibliometric Analysis

Few-Shot Learning for Misinformation Detection Based on Contrastive Models

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

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