Automatic cyberbullying detection: A systematic review

Rosa, Hugo; Pereira, Nádia Salgado; Ribeiro, Ricardo; Ferreira, Paula Costa; Carvalho, João Paulo; Oliveira, Sofia; Coheur, Luísa; Paulino, Paula; Simão, Ana Margarida Veiga; Trancoso, Isabel

doi:10.1016/j.chb.2018.12.021

Cited by 210 publications

(146 citation statements)

References 25 publications

Supporting

Mentioning

123

Contrasting

Unclassified

Order By: Relevance

“…Labeling process. In the literature, we found that some researchers [33,[38][39][40] used the web service of Amazon's Mechanical Turk and paid for anonymous online workers to manually label comments, reviews, words, images, among others. However, in [41], tagging by workers from Amazon's Mechanical Turk showed that at least 2.5% of reviews, classified as "no cyber-bullying" should be tagged as "cyber-bullying", for this reason, they sought support from graduate students and some undergraduates of psychology.…”

Section: Methodsmentioning

confidence: 99%

Classification of Cyber-Aggression Cases Applying Machine Learning

2019

View full text Add to dashboard Cite

The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a social problem identified as cyber-aggression, or in some cases called cyber-bullying. This paper presents research to classify situations of cyber-aggression on social networks, specifically for Spanish-language users of Mexico. We applied Random Forest, Variable Importance Measures (VIMs), and OneR to support the classification of offensive comments in three particular cases of cyber-aggression: racism, violence based on sexual orientation, and violence against women. Experimental results with OneR improve the comment classification process of the three cyber-aggression cases, with more than 90% accuracy. The accurate classification of cyber-aggression comments can help to take measures to diminish this phenomenon.

show abstract

Section: Methodsmentioning

confidence: 99%

Classification of Cyber-Aggression Cases Applying Machine Learning

2019

View full text Add to dashboard Cite

show abstract

“…On the other hand, typically cyberbullying detection takes place only after the victim has been exposed to the damaging context [7].…”

Section: ) Policy Control Function (Pcf)mentioning

confidence: 99%

“…In this work, in order to detect cyberbullying with AI techniques we use the results of a recent work by Rosa et al [7]. We particularly use the results of Fig.…”

Section: B Phasementioning

confidence: 99%

“…Although, there have been several studies on how to detect cyberbullying with the help of Artificial Intelligence (AI) methods [7], [8], it is still difficult to instantly detect a harmful electronic context [7], and protect victim's of cyberbullying to receive such context. On the other hand, individual's messages and online comments are considered to be private.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Privacy Preserving Cyberbullying Prevention with AI Methods in 5G Networks

Ramezanian

Niemi

2019

2019 25th Conference of Open Innovations Association (FRUCT)

View full text Add to dashboard Cite

Children and teenagers that have been a victim of bullying can possibly suffer its psychological effects for a lifetime. With the increase of online social media, cyberbullying incidents have been increased as well. In this paper we discuss how we can detect cyberbullying with AI techniques, using term frequencyinverse document frequency. We label messages as benign or bully. We want our method of cyberbullying detection to be privacypreserving, such that the subscribers' benign messages should not be revealed to the operator. Moreover, the operator labels subscribers as normal, bully and victim. The operator utilizes policy control in 5G networks, to protect victims of cyberbullying from harmful traffic.

show abstract

“…Facebook affects not only the emotions that we express on these platforms but also our general wellbeing, for example, through consuming toxic content or experiencing cyberbullying attacks [23], [24], [30].…”

Section: Introductionmentioning

confidence: 99%