Detecting the Usage of Vulgar Words in Cyberbully Activities from Twitter

Tarmizi, Nursyahirah; Saee, Suhaila; Ibrahim, Dayang Hanani Abang

doi:10.18517/ijaseit.10.3.10645

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…The bot also scans videos posted and blurs their thumbnails. Some social media platforms such as Twitter further provide a warning to users that the content is graphic and may be inappropriate to some audiences [25]. In text-based classification, social media sites have a big data dictionary of offensive phrases.…”

Section: Literature Reviewmentioning

confidence: 99%

The Sustainable Effect of Artificial Intelligence and Parental Control on Children’s Behavior While Using Smart Devices’ Apps: The Case of Saudi Arabia

Alrusaini

Beyari²

2022

Sustainability

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The study was necessitated by the unprecedented consumption of smart devices by children in Saudi Arabia, which has been a concern to parents and other stakeholders. It investigated the way that game apps, social media apps, and video-streaming apps impact child development. It also examined the roles played by artificial intelligence control and parental control in enhancing the sustainability of children’s behavior amidst smart technologies. The theories underpinning this research were the theory of reasoned action and the theory of planned behavior. The population of the study was 1,616,755 households in the eastern, western, and central regions. The researcher used an online survey to capture the sentiments of a sample of 415 parents who had given their children at least one smart device. The primary questionnaire focused on game apps, social media apps, video-streaming apps, artificial intelligence control, parental control, and the sustainability of child behavior. On the other hand, a separate questionnaire designed specifically to capture demographic information was also drafted. The structural equation model (SEM) was preferred, as it depicted the moderating roles of artificial intelligence control and parental control by using SPSS AMOS software. Findings established that games, social media, and video-streaming applications negatively affected the sustainability of child behavior. The findings presented in this paper show that the moderating effect of artificial intelligence control was more statistically significant than parental controls in influencing the sustainability of child behavior. Moreover, the results show that the greatest effect on children’s behavior were social media, video-streaming, and games apps. respectively. Nevertheless, both approaches resulted in positive child behavior. Hence, the study concluded that using artificial intelligence control is more effective than relying on parental controls to enhance the behavioral sustainability of children with smart device applications in Saudi Arabia.

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Section: Literature Reviewmentioning

confidence: 99%

The Sustainable Effect of Artificial Intelligence and Parental Control on Children’s Behavior While Using Smart Devices’ Apps: The Case of Saudi Arabia

Alrusaini

Beyari²

2022

Sustainability

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“…Cyberbullying has been given several definitions, including those related to cyberstalking [10]. Cyberstalking is defined as 'stalking other people information to make a false accusation, monitoring, identity theft, threats, and create data destruction or manipulation' [11]. However, some scholars have classified cyberstalking as a type of cyberbullying [8], [9], [11]- [13].…”

Section: Definition Of Cyberbullyingmentioning

confidence: 99%

“…Cyberstalking is defined as 'stalking other people information to make a false accusation, monitoring, identity theft, threats, and create data destruction or manipulation' [11]. However, some scholars have classified cyberstalking as a type of cyberbullying [8], [9], [11]- [13]. Cyberbullying has emerged with the rapid development of social networks [14].…”

Section: Definition Of Cyberbullyingmentioning

confidence: 99%

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Implementation of Hyperparameter Optimisation and Over-Sampling in Detecting Cyberbullying Using Machine Learning Approach

Ali

Mohd

Fauzi

et al. 2021

MJCS

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Online social networks have become a necessity to everyone around the world. Particularly, online social networks have enabled us to connect to one another regardless of time, for as long as we have social media and social networking as platforms for broadcasting information and communicating, respectively. However, this evolution has resulted in people possibly committing various cybercrimes, such as cyberbullying. To address this issue, machine learning can be utilised to counter cyberbullying in online social networks. Thus, this study proposed a framework with a set of features consisting of word and character term frequency–inverse document frequency and word embedding by using Word2vec and six types of list terms: profane words, proper nouns, negation words, ‘allness’ term, diminisher words and intensifier words. These features were divided into four groups before being fed into the linear support vector classifier to train our model using ASKfm as data set in hyperparameter tuning and over-sampling environment. Results indicated that the proposed framework provided significant outcomes, in which the highest percentage of area under curve is 99.24% and F-measure is 97.38% as performed by our trained model.

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“…There are numerous definitions of cyberbullying, some of which include cyberstalking [5]. Cyberstalking includes "stalking other people's information to produce a false charge, monitoring, identity theft, threats, and causing data destruction or alteration" [6]. However, some academics [7], [8] consider internet tracking to be a form of online bullying.…”

Section: Introductionmentioning

confidence: 99%

Arabic cyberbullying detecting using ensemble deep learning technique

Ahmed,

Maher,

Khudhur

2023

IJEECS

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<span lang="EN-US">There has been a huge growth in recent years interest in studies on abusive language and cyberbullying detection due to its effects on both individual victims and societies. Hate speech, bullying, racism, aggressive content, harassment and other forms of abuse have all significantly increased as a result of Facebook, Instagram, and other social media platforms (SMPs). Since there is a significant need to detect, control, and prohibit the circulation of offensive content on social networking sites, we undertook this study to automate the identification of abusive language or cyberbullying. Arabic data set is balanced and will be used in the offensive detection process. Recently, ensemble machine learning has been used to increase the effectiveness of categorization models. Arabic detection is more precise given that each spatial feature text can make references to every other contextual piece of information. The authors utilized a model that merged convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) and inverse document frequency gated recurrent unit (GRU) in a hybrid fashion without any post-processing. Our work outperformed every other publicly released cutting-edge ensemble model in the specifications of the official deep learning challenge. The findings indicate that the three-layer inverse document frequency long short-term memory (LSTM) classifier surpassed other classifiers in accuracy with a score of 92.75% compared to different algorithms.</span>

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Detecting the Usage of Vulgar Words in Cyberbully Activities from Twitter

Cited by 4 publications

References 9 publications

The Sustainable Effect of Artificial Intelligence and Parental Control on Children’s Behavior While Using Smart Devices’ Apps: The Case of Saudi Arabia

The Sustainable Effect of Artificial Intelligence and Parental Control on Children’s Behavior While Using Smart Devices’ Apps: The Case of Saudi Arabia

Implementation of Hyperparameter Optimisation and Over-Sampling in Detecting Cyberbullying Using Machine Learning Approach

Arabic cyberbullying detecting using ensemble deep learning technique

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