Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

Abstract

In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

“…Both methods are used to learn the usage context of a word. Global vector space [20], [25], [26], [27], [28], [29], [30] Distance Measures Edit-Distance [20] Word Embedding Word2Vec [31], [32], [28], [33], [34], [35], [30], [36], [37], [38], [39] Skip-gram [25] CBoW [25], [32] BoW [40], [31], [33], [34] TF-IDF [26], [27] FastText [25], [36] GLoVe [41], [42], [37] LSHWE [37] Vulgarity/Hate Features [43], [25], [32], [33], [44] Sentiment Sentiment Analysis [27], [45], [32], [33], [41], [46], [30], [39] User Profile [27],…”

“…Yuvaraj et al [46] proposed a framework integrating artificial neural network (ANN) and deep reinforcement learning (DRL) and achieved an accuracy of 98% on the Twitter data (30, Most of the research mentioned above focuses on detecting cyberbullying on datasets collected from one or two social media platforms. Bruwaene et al [78] collected a text-based dataset from VISR tool of SafeToNet that monitors the social media activity of a child on various social media platforms.…”

Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

“…Big data involves the process of combining enormous volume of data and analyzing it using the complex algorithms [11][12][13]. The big data analytic deals with the usage of advanced techniques on a vast dataset for discovering the relevant pattern and information [14][15][16].…”

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

“…This literature survey of various techniques suggests that the deep learning-based strategies are being focused on in the present scenario in order to provide better results for various image recognition and classification approaches [19][20][21]. The DR classification-based approaches have also shifted their concern towards the deep learning as the previously used traditional methods were labor intensive, require professional information [22][23][24]. However, despite of various advantages of DNN methods, still it possesses the challenge for healthcare application and its practical implementation.…”