Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification

Yuvaraj, N.; Chang, Victor; Gobinathan, Balasubramanian; Pinagapani, Arulprakash; Kannan, Srihari; Rajan, Arsath Raja

doi:10.1016/j.compeleceng.2021.107186

Cited by 54 publications

(16 citation statements)

References 25 publications

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“…However, the CNN+LSTM and CNN+GRU complexity is higher and might not be effective in handling larger datasets. Natarajan Yuvaraj et al [18] proposed a new classification model for CB detection from Twitter data. It used deep decision-tree classification with multi-feature based AI for tweet classification.…”

Section: Related Workmentioning

confidence: 99%

“…The performance index is denoted by 𝑉 𝐹 (𝑥), and the average of performance is denoted by 𝑉 𝜇 (𝑥). At the end of each iteration in DEA, the average Sum of Square Errors (SSE) of ith iteration is computed as given in Eq (18). 𝑆𝑆𝐸 𝑖 = {𝑉 𝜇 1 (𝑥), 𝑉 𝜇 2 (𝑥), … , 𝑉 𝜇 𝑛 (𝑥)} ( 18)…”

Section: 𝐸 = (𝑇 𝑘 − 𝑌 𝑘 ) (15)mentioning

confidence: 99%

“…Text classification based on supervised machine learning (ML) models are commonly used for classifying tweets into bullying and non-bullying tweets [8] [9], [10], [11], [12], [13], [14], [15], [16],and [17]. Deep learning (DL) based classifiers have also been used for classifying tweets into bullying and non-bullying tweets [18], [19], [20], [21], [22], and [7]. Supervised classifiers have low performance in case the class labels are unchangeable and are not relevant to the new events [23].…”

Section: Introductionmentioning

confidence: 99%

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DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

et al. 2022

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Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for finetuning the Elman RNN's parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.

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Section: Related Workmentioning

confidence: 99%

Section: 𝐸 = (𝑇 𝑘 − 𝑌 𝑘 ) (15)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

et al. 2022

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“…Talpur and O'Sullivan [ 14 ] created a supervised machine learning strategy for detecting CB and categorizing its severity on Twitter, which they published in Nature. The text classification engine created by Yuvaraj et al [ 15 ] that preprocesses tweets, eliminates noisy data and other background information, extracts the desired features, and categorizes without overfitting the data is described in detail below. This research advances a novel DDT strategy that processes input components by utilizing the DNN hidden layer as tree nodes, as demonstrated in previous research.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media

Neelakandan¹,

Sridevi

Chandrasekaran

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the ease with which the internet and cell phones can be accessed, online social networks (OSN) and social media have seen a significant increase in popularity in recent years. Security and privacy, on the other hand, are the key concerns in online social networks and other social media platforms. On the other hand, cyberbullying (CB) is a serious problem that needs to be addressed on social media platforms. Known as cyberbullying (CB), it is defined as a repetitive, purposeful, and aggressive reaction performed by individuals through the use of information and communication technology (ICT) platforms such as social media platforms, the internet, and cell phones. It is made up of hate messages that are sent by e-mail, chat rooms, and social media platforms, which are accessed through computers and mobile phones. The detection and categorization of CB using deep learning (DL) models in social networks are, therefore, crucial in order to combat this trend. Feature subset selection with deep learning-based CB detection and categorization (FSSDL-CBDC) is a novel approach for social networks that combines deep learning with feature subset selection. The suggested FSSDL-CBDC technique consists of a number of phases, including preprocessing, feature selection, and classification, among others. Additionally, a binary coyote optimization (BCO)-based feature subset selection (BCO-FSS) technique is employed to select a subset of features that will increase classification performance by using the BCO algorithm. Additionally, the salp swarm algorithm (SSA) is used in conjunction with a deep belief network (DBN), which is known to as the SSA-DBN model, to detect and characterize cyberbullying in social media networks and other online environments. The development of the BCO-FSS and SSA-DBN models for the detection and classification of cyberbullying highlights the originality of the research. A large number of simulations were carried out to illustrate the superior classification performance of the proposed FSSDL-CBDC technique. The SSA-DBN model has exhibited superior accuracy to the other algorithms, with a 99.983 % accuracy rate. Overall, the experimental results revealed that the FSSDL-CBDC technique beats the other strategies in a number of different aspects.

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“…The CNN-BiLSTM model outperformed all the baseline deep learning models. A cyberbully detection model was proposed by [ 17 ] to improve manual monitoring for cyberbullying on social media. In this paper OCR was used to analyze image character to determine the impact of image-based cyberbullying on an individual basis, which was further tested on a dummy system.…”

Section: Literature Reviewmentioning

confidence: 99%

An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques

et al. 2022

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Nowadays, a lot of people indulge themselves in the world of social media. With the current pandemic scenario, this engagement has only increased as people often rely on social media platforms to express their emotions, find comfort, find like-minded individuals, and form communities. With this extensive use of social media comes many downsides and one of the downsides is cyberbully. Cyberbullying is a form of online harassment that is both unsettling and troubling. It can take many forms, but the most common is a textual format. Cyberbullying is common on social media, and people often end up in a mental breakdown state instead of taking action against the bully. On the majority of social networks, automated detection of these situations necessitates the use of intelligent systems. We have proposed a cyberbullying detection system to address this issue. In this work, we proposed a deep learning framework that will evaluate real-time twitter tweets or social media posts as well as correctly identify any cyberbullying content in them. Recent studies has shown that deep neural network-based approaches are more effective than conventional techniques at detecting cyberbullying texts. Additionally, our application can recognise cyberbullying posts which were written in English, Hindi, and Hinglish (Multilingual data).

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Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification

Cited by 54 publications

References 25 publications

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media

An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques

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