Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges

Al-Garadi, Mohammed Ali; Hussain, Mohammad Rashid; Khan, Nawsher; Murtaza, Ghulam; Nweke, Henry Friday; Ali, Ihsan; Mujtaba, Ghulam; Chiroma, Haruna; Khattak, Hasan Ali; Gani, Abdullah

doi:10.1109/access.2019.2918354

Cited by 123 publications

(70 citation statements)

References 110 publications

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“…Main focus and contribution of the current study was to provide systematic way to apply level of severity in cyberbullying behavioural text using multi-class classification. [ 81 ] and [ 82 ] worked in this area focusing on the binary classification and did not highlight the systematic procedure to go about detecting cyberbullying severity. Moreover, aim of our study was to compare well-known approaches that have been discussed in [ 48 ], rather than results from their datasets.…”

Section: Discussionmentioning

confidence: 99%

Cyberbullying severity detection: A machine learning approach

Talpur

O’Sullivan

2020

PLoS ONE

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With widespread usage of online social networks and its popularity, social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. In this study, we have proposed a cyberbullying detection framework to generate features from Twitter content by leveraging a pointwise mutual information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation. Extracted features were applied with Naïve Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machine algorithms. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa, classifier accuracy and f-measure metrics, as well as in a binary setting. These results indicate that our proposed framework provides a feasible solution to detect cyberbullying behavior and its severity in online social networks. Finally, we compared the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection.

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Section: Discussionmentioning

confidence: 99%

Cyberbullying severity detection: A machine learning approach

Talpur

O’Sullivan

2020

PLoS ONE

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“…excluding other users from a group or conspiring against them), whereas men tend to use more threatening words. On the other hand, as pointed out by Can and Atlas (2019), social media information has provided interesting hints for a better understanding of the online user behaviors associated with cyberbullying [58]. They reported that posts with cyberbullying contents received comments more frequently and fewer likes per post than other posts [58].…”

Section: A Main Featuresmentioning

confidence: 96%

“…Therefore, ML algorithms could serve to early and automatically detect cyberbullying, and to foster intervention protocols' timely activation. In the last decade, researchers have tested a variety of ML techniques such as victims' sentiment informed analysis, textual, and semantic analysis, and user features' analysis (e.g., gender) [46,47,58,49]. Plus, these methods allowed to detect a variety of cyberbullying outcomes including binary classifications (e.g., being or not-being involved), role identification in cyberbullying dynamics and the severity of consequences [50,51,52].…”

Section: B Machine Learning and Cyberbullyingmentioning

confidence: 99%

“…According to Can and Atlas (2019), cyberbullying mainly occurs when social media texts include specific conversation themes such as death, appearance, religion, and sexual content [58]. This aspect might be exaggerated by the disinhibition effect provided by the Internet environment, which leads users to express more violently than they would do into face-to-face interactions [43,59].…”

Section: A Main Featuresmentioning

confidence: 99%

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Cyberbullying Detection Through Machine Learning: Can Technology Help to Prevent Internet Bullying?

Angelis*¹,

Perasso²

2020

IJMH

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Cyberbullying constitutes a threat to adolescents’ psychosocial wellbeing that developed alongside technological progress. Detecting online bullying cases is still an issue because most of victims and bystanders do not timely report cyberbullying episodes to adults. Therefore, automatized technologies may play a critical role in detecting cyberbullying through the use of Machine Learning (ML). ML covers a broad range of techniques that enables systems to quickly access and learn from data, and to make decisions about complex problems. This contribution aims at deepening the role of ML in cyberbullying detection and prevention. Specifically, the following issues are addressed: i. identifying the features most frequently considered to develop ML models predicting cyberbullying; ii. identifying the most used ML algorithms and their evaluation methods; iii. understanding the implication of ML for prevention; iv. highlighting the main theoretical and methodological issues of ML algorithms in predicting cyberbullying. To answer these research questions, a systematic review of literature reviews, from a total of n=186 records from online databanks, has been conducted. Ten literature reviews have been elected to analyze and discuss evidence about ML preventative potential against cyberbullying. Most of the models used content-based features to predict cyberbullying. The majority of these features includes words written in social network posts, whereas Support Vector Machine, Naïve Bayes, and Convolutional Neural Networks are the most used alghorithms. Methodological and technical issues have been critically discussed. ML represents an innovative preventative strategy that may optimize and integrate educational programs for adolescents and be the starting point of the development of technology-based automatized detection strategies. Future research is challenged to develop algorithms capable of detecting cyberbullying from several multimedia sources.

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“…Therefore, there is a need for an architecture that preserves the log privacy, provenance, and confidentiality along with scalability of edge node integration in an efficient fashion. The subsequent sections provide the detail of proposed scheme based on discovered solutions along with threat model and security requirements [49,50].…”

Section: Related Workmentioning

confidence: 99%

Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies

et al. 2020

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Collecting and preserving the smart environment logs connected to cloud storage is challenging due to the black-box nature and the multi-tenant cloud models which can pervade log secrecy and privacy. The existing work for log secrecy and confidentiality depends on cloud-assisted models, but these models are prone to multi-stakeholder collusion problems. This study proposes ’PLAF,’ a holistic and automated architecture for proactive forensics in the Internet of Things (IoT) that considers the security and privacy-aware distributed edge node log preservation by tackling the multi-stakeholder issue in a fog enabled cloud. We have developed a test-bed to implement the specification, as mentioned earlier, by incorporating many state-of-the-art technologies in one place. We used Holochain to preserve log integrity, provenance, log verifiability, trust admissibility, and ownership non-repudiation. We introduced the privacy preservation automation of log probing via non-malicious command and control botnets in the container environment. For continuous and robust integration of IoT microservices, we used docker containerization technology. For secure storage and session establishment for logs validation, Paillier Homomorphic Encryption, and SSL with Curve25519 is used respectively. We performed the security and performance analysis of the proposed PLAF architecture and showed that, in stress conditions, the automatic log harvesting running in containers gives a 95% confidence interval. Moreover, we show that log preservation via Holochain can be performed on ARM-Based architectures such as Raspberry Pi in a very less amount of time when compared with RSA and blockchain.

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Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges

Cited by 123 publications

References 110 publications

Cyberbullying severity detection: A machine learning approach

Cyberbullying severity detection: A machine learning approach

Cyberbullying Detection Through Machine Learning: Can Technology Help to Prevent Internet Bullying?

Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies

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