Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, the opinion analysis procedure is prone to errors. Concerns about the integrity of analytics have grown as the usage of automated social media analysis tools has expanded. According to preliminary research, sarcastic statements alone have significantly reduced the accuracy of automatic sentiment analysis. Sarcastic phrases also impact automatic fake news detection leading to false positives. Various individual natural language processing techniques have been proposed earlier, but each has textual context and proximity limitations. They cannot handle diverse content types. In this research paper, we propose a novel hybrid sentence embedding-based technique using an autoencoder. The framework proposes using sentence embedding from long short term memory-autoencoder, bidirectional encoder representation transformer, and universal sentence encoder. The text over images is also considered to handle multimedia content such as images and videos. The final framework is designed after the ablation study of various hybrid fusions of models. The proposed model is verified on three diverse real-world social media datasets—Self-Annotated Reddit Corpus (SARC), headlines dataset, and Twitter dataset. The accuracy of 83.92%, 90.8%, and 92.80% is achieved. The accuracy metric values are better than previous state-of-art frameworks.

Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.

This study presents a practical framework for implementing blockchain technology, specifically smart contracts, to optimise operations and enhance financial performance in the Consumer Packaged Goods (CPG) sector. It identifies best practices for operational efficiency and outlines the structural flow and challenges of implementing smart contracts in a small-scale CPG company. While blockchain is often associated with cryptocurrency, its value lies in enhancing core business processes such as vendor selection, procurement and legal compliance monitoring. The framework integrates blockchain-enabled smart contracts with project management lifecycle updates to streamline operations, enhance cash flow and reduce the Cost of Goods Sold (COGS). It highlights how procurement processes, legal requirements and vendor management can be streamlined through smart contracts, providing transparency, reducing delays and ensuring regulatory compliance. Blockchain is a decentralised database, and its applications span procurement, production processes and inventory management. However, leveraging blockchain effectively requires smart contracts. Integrating these contracts with project management tools ensures efficient operations and measurable financial metrics. This interdisciplinary approach combines technology, business law and project management to deliver actionable insights. The study highlights how modest operational efficiencies can drive profitability in low-margin industries, such as CPG, and establishes a foundation for future implementation studies across other sectors.