Cyberbullying Detection Through Sentiment Analysis

Atoum, Jalal Omer

doi:10.1109/csci51800.2020.00056

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…Sentiment and emotional features capture the sentiment, subjectivity, polarity, and expression of emotion embedded in the text. Sentiment analysis presented that text polarity improved the cyberbullying detection model [82]. The Sentiment Informed Cyberbullying Detection (SICD) developed by Dani et al [83] outperformed other baseline models such as LS, Lasso, and SVM, whereby the learning framework took the sentiment and user relationship information of the post into account.…”

Section: F Features Used In Automated Cyberbullying Detectionmentioning

confidence: 99%

Cyberbullying Detection in Social Networks: A Comparison Between Machine Learning and Transfer Learning Approaches

Teng

Varathan

2023

IEEE Access

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Information and Communication Technologies fueled social networking and facilitated communication. However, cyberbullying on the platform had detrimental ramifications. The user-dependent mechanisms like reporting, blocking, and removing bullying posts online is manual and ineffective. Bagof-words text representation without metadata limited cyberbullying post text classification. This research developed an automatic system for cyberbullying detection with two approaches: Conventional Machine Learning and Transfer Learning. This research adopted AMiCA data encompassing significant amount of cyberbullying context and structured annotation process. Textual, sentiment and emotional, static and contextual word embeddings, psycholinguistics, term lists, and toxicity features were used in the conventional Machine Learning approach. This study was the first to use toxicity features to detect cyberbullying. This study is also the first to use the latest psycholinguistics features from the Linguistic Inquiry and Word (LIWC) 2022 tool, as well as Empath's lexicon, to detect cyberbullying. The contextual embeddings of ggeluBert, tnBert, and DistilBert have alike performance, however DistilBert embeddings were elected for higher F-measure. Textual features, DistilBert embeddings, and toxicity features that struck new benchmark were the top three unique features when fed individually. The model's performance was boosted to F-measure of 64.8% after feeding with a combination of textual, sentiment, DistilBert embeddings, psycholinguistics, and toxicity features to the Logistic Regression model that outperforms Linear SVC with faster training time and efficient handling of high-dimensionality features. Transfer Learning approach was by fine-tuning optimized version Pre-trained Language Models namely, DistilBert, DistilRoBerta, and Electra-small which were found to have speedier training computation than their base form. The fine-tuned DistilBert resulted with the highest F-measure of 72.42%, surpassing CML. Our research concluded that Transfer Learning was the best for uplifted performance and lesser effort as feature engineering and resampling was omitted.

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Section: F Features Used In Automated Cyberbullying Detectionmentioning

confidence: 99%

Cyberbullying Detection in Social Networks: A Comparison Between Machine Learning and Transfer Learning Approaches

Teng

Varathan

2023

IEEE Access

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“…From Twitter, Atoum [140] gathered two datasets (Dataset-1 and Dataset-2) one month apart. Twitter dataset 1 contains 6463 tweets, 2521 of which are about cyberbullying and 3942 are not.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

“…Atoum [140] developed an effective sentiment analysis and language modeling technique to identify cyberbullying in tweets. On the basis of two tweet datasets, various machine learning algorithms are compared and contrasted.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

Sahana V

2023

IJRITCC

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In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content. Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media.

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“…Cyberbullying is the group and individual user using information and communication technologies for the purpose of harassing other users [4]. Cyberbullying is widely acknowledged as a serious issue, with victims displaying a dramatically increased risk of suicidal ideation [5].…”

Section: Introductionmentioning

confidence: 99%

Detecting Cybercrime: An Evaluation of Machine Learning and Deep Learning Using Natural Language Processing Techniques on the Social Network

Amer

Siddiqui

Athamena

2022

Preprint

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The widespread use of online social networks has culminated in across-the-board social communication among users, resulting in a considerable amount of user-generated contact data. Cybercrime has become a significant issue in recent years with the rise of online communication and social network. Cybercrime has lately been identified as a severe national psychological concern among platform users, and building a reliable detection model is crucial. Cyberbullying is the phrase used to describe such online harassment, insults, and attacks. It has become challenging to identify such unauthorized content due to the massive number of user-generated content. Because deep neural networks have various advantages over conventional machine learning approaches, researchers are turning to them more frequently to identify cyberbullying. Deep learning and machine learning have several uses in text classification. This article suggested the novel neural network model through parameters of an algorithmic and optimization comparative analysis of nine category approaches, four neural networks, and five machine learning, in two scenarios with real-world datasets of cyberbullying. Moreover, this work also analyzes the impact of word embedding and feature extraction techniques based on text mining and NLP on algorithms' performances. We performed extensive experiments on the two scenarios with a split dataset to demonstrate the merit of this research, comparing nine classification approaches through five feature extraction techniques. Our proposed cybercriminal detection model using neural networks, deep learning, and machine learning outperforms the existing state-of-the-art method of cybercriminal detection in terms of accuracy achieving higher performance.

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Cyberbullying Detection Through Sentiment Analysis

Cited by 17 publications

References 11 publications

Cyberbullying Detection in Social Networks: A Comparison Between Machine Learning and Transfer Learning Approaches

Cyberbullying Detection in Social Networks: A Comparison Between Machine Learning and Transfer Learning Approaches

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

Detecting Cybercrime: An Evaluation of Machine Learning and Deep Learning Using Natural Language Processing Techniques on the Social Network

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