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In recent digital era, social media sites have been commonly used by majority of people to generate massive quantities of textual data. Sarcasm can be treated as a kind of sentiment, which generally expresses the opposite of what has been anticipated. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). The recent advancements of deep learning (DL) models influence neural networks (NN) in learning the lexical as well as contextual features, eradicating the necessity of hand-crafted features for sarcasm detection. With this motivation, this article designs an automated sarcasm detection and classification tool using hyperparameter tuned deep learning (ASDC-HPTDL) model for social media. The proposed ASDC-HPTDL technique primarily involves pre-processing stage to transform the data into useful format. At the next stage of pre-processing, the pre-processed data is converted into the feature vector by Glove Embedding's technique. Followed by, attention bidirectional gated recurrent unit (ABiGRU) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the ABiGRU technique, a hyperparameter tuning process using improved artificial flora algorithm (IAFO) is employed, shows the novelty of the work. The proposed model is validated using the benchmark dataset and the results are examined interms of precision, recall, accuracy, and F1-score.

The task of identifying anomalous users on attributed social networks requires the detection of users whose profile attributes and network structure significantly differ from those of the majority of the reference profiles. GNN-based models are well-suited for addressing the challenge of integrating network structure and node attributes into the learning process because they can efficiently incorporate demographic data, activity patterns, and other relevant information. Aggregate operations, such as sum or mean pooling, are utilized by Graph Neural Networks (GNNs) to combine the representations of neighboring nodes within a graph. However, these aggregate operations can cause problems in detecting anomalous nodes. There are two main issues to consider when utilizing aggregate operations in GNNs. Firstly, the presence of anomalous neighboring nodes may affect the representation of normal nodes, leading to false positives. Secondly, anomalous nodes may be overlooked as their representation is flattened during the aggregate operation, leading to false negatives. The proposed approach, AnomEn, is a robust graph neural network developed for anomaly detection. It addresses the challenges of false positives and false negatives using a weighted aggregate mechanism. This mechanism is designed to differentiate between a node’s own features and the features of its neighbors by placing greater emphasis on a node’s own features and less emphasis on its neighbors’ features. The system can preserve the node’s original characteristics, whether the node is normal or anomalous. This work proposes not only a robust graph neural network, namely, AnomEn, but also specific anomaly detection structures for nodes and edges. The proposed AnomEn method serves as the encoder in the node and edge anomaly detection architectures and was tested on multiple datasets. Experiments were conducted to validate the effectiveness of the proposed method as a graph neural network encoder. The findings demonstrated the robustness of the proposed method in detecting anomalies. The proposed method outperforms other existing methods in node anomaly detection tasks by 5.63% and edge anomaly detection tasks by 7.87%.

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