An image and text-based multimodal model for detecting fake news in OSN’s

Uppada, Santosh Kumar; Patel, Parth; Sivaselvan, B.

doi:10.1007/s10844-022-00764-y

Cited by 16 publications

(6 citation statements)

References 56 publications

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“…Due to the constant growth in social media platforms, information generation is not limited to only text. It is observed that visual modalities such as images and videos tend to attract more audiences on social media and spread faster than text-based information (Uppada et al, 2022). Hence, an information assessment system must be able to work with multiple modalities.…”

Section: Aied Systems For Information Assessment -Requirements and In...mentioning

confidence: 99%

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

Ali Khan,

Kauttonen,

Aunimo

et al. 2024

JITE:Research

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Aim/Purpose: The purpose of this paper is to address the challenges posed by disinformation in an educational context. The paper aims to review existing information assessment techniques, highlight their limitations, and propose a conceptual design for a multimodal, explainable information assessment system for higher education. The ultimate goal is to provide a roadmap for researchers that meets current requirements of information assessment in education. Background: The background of this paper is rooted in the growing concern over disinformation, especially in higher education, where it can impact critical thinking and decision-making. The issue is exacerbated by the rise of AI-based analytics on social media and their use in educational settings. Existing information assessment techniques have limitations, requiring a more comprehensive AI-based approach that considers a wide range of data types and multiple dimensions of disinformation. Methodology: Our approach involves an extensive literature review of current methods for information assessment, along with their limitations. We then establish theoretical foundations and design concepts for EMIAS based on AI techniques and knowledge graph theory. Contribution: We introduce a comprehensive theoretical framework for an AI-based multimodal information assessment system specifically designed for the education sector. It not only provides a novel approach to assessing information credibility but also proposes the use of explainable AI and a three-pronged approach to information evaluation, addressing a critical gap in the current literature. This research also serves as a guide for educational institutions considering the deployment of advanced AI-based systems for information evaluation. Findings: We uncover a critical need for robust information assessment systems in higher education to tackle disinformation. We propose an AI-based EMIAS system designed to evaluate the trustworthiness and quality of content while providing explanatory justifications. We underscore the challenges of integrating this system into educational infrastructures and emphasize its potential benefits, such as improved teaching quality and fostering critical thinking. Recommendations for Practitioners: Implement the proposed EMIAS system to enhance the credibility of information in educational settings and foster critical thinking among students and teachers. Recommendation for Researchers: Explore domain-specific adaptations of EMIAS, research on user feedback mechanisms, and investigate seamless integration techniques within existing academic infrastructure. Impact on Society: This paper’s findings could strengthen academic integrity and foster a more informed society by improving the quality of information in education. Future Research: Further research should investigate the practical implementation, effectiveness, and adaptation of EMIAS across various educational contexts.

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Section: Aied Systems For Information Assessment -Requirements and In...mentioning

confidence: 99%

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

Ali Khan,

Kauttonen,

Aunimo

et al. 2024

JITE:Research

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“…Uppada et al [40] presented a framework that flagged the fake news by embedding visual data with text. The suggested framework performed on the data obtained from a benchmark dataset.…”

Section: Kumari and Ekbalmentioning

confidence: 99%

Multimodal fake news detection using hyperparameter-tuned BERT and ResNet110

2024

IJATEE

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“…The analysis of social media data and the information within posts, particularly text embedded in images, is gaining prominence in forensic investigations [17,18]. This process involves extracting text from images and then analyzing its content.…”

Section: Literature Reviewmentioning

confidence: 99%

Graph convolution networks for social media trolls detection use deep feature extraction

Asif,

Al-Razgan,

Ali

et al. 2024

J Cloud Comp

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This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate relationships inherent in social media data. The practical implications of our work are significant, despite some limitations in the model's performance. While the model accurately identifies toxic content more than half of the time, it struggles with precision, correctly identifying positive instances less than 50% of the time. Additionally, its ability to detect all positive cases (recall) is limited, capturing only 40% of them. The F1-score, which is a measure of the model's balance between precision and recall, stands at around 0.4, indicating a need for further refinement to enhance its effectiveness. This research offers a promising step towards more effective monitoring and moderation of toxic content on social platforms.

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An image and text-based multimodal model for detecting fake news in OSN’s

Cited by 16 publications

References 56 publications

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

Multimodal fake news detection using hyperparameter-tuned BERT and ResNet110

Graph convolution networks for social media trolls detection use deep feature extraction

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