Automatic Monitoring and Prevention of Cyberbullying

Sugandhi, Rekha; Pande, Anurag; Agrawal, Abhishek; Bhagat, Husen

doi:10.5120/ijca2016910408

Cited by 22 publications

(13 citation statements)

References 4 publications

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“…The remaining 17 studies were ambiguous when providing details to annotators for labeling purposes or when determining whether annotators were experts in the domain of cyberbullying. From the five studies (Bayzick et al, 2011;Hosseinmardi et al, 2015;Ptaszynski et al, 2018;Sugandhi et al, 2016;Van Hee et al, 2015) that provided some instruction, the annotators were given definitions of cyberbullying and/or given context to the content they were labeling. Rosa et al (2019) also found that annotators for cyberbullying datasets, when available, were frequently students or random individuals on MTurk without specific qualifications.…”

Section: Annotation Of Existing Cyberbullying Datasetsmentioning

confidence: 99%

Curating Cyberbullying Datasets: a Human-AI Collaborative Approach

Gomez

Sztainberg

Trana

2021

Int Journal of Bullying Prevention

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Cyberbullying is the use of digital communication tools and spaces to inflict physical, mental, or emotional distress. This serious form of aggression is frequently targeted at, but not limited to, vulnerable populations. A common problem when creating machine learning models to identify cyberbullying is the availability of accurately annotated, reliable, relevant, and diverse datasets. Datasets intended to train models for cyberbullying detection are typically annotated by human participants, which can introduce the following issues: (1) annotator bias, (2) incorrect annotation due to language and cultural barriers, and (3) the inherent subjectivity of the task can naturally create multiple valid labels for a given comment. The result can be a potentially inadequate dataset with one or more of these overlapping issues. We propose two machine learning approaches to identify and filter unambiguous comments in a cyberbullying dataset of roughly 19,000 comments collected from YouTube that was initially annotated using Amazon Mechanical Turk (AMT). Using consensus filtering methods, comments were classified as unambiguous when an agreement occurred between the AMT workers’ majority label and the unanimous algorithmic filtering label. Comments identified as unambiguous were extracted and used to curate new datasets. We then used an artificial neural network to test for performance on these datasets. Compared to the original dataset, the classifier exhibits a large improvement in performance on modified versions of the dataset and can yield insight into the type of data that is consistently classified as bullying or non-bullying. This annotation approach can be expanded from cyberbullying datasets onto any classification corpus that has a similar complexity in scope.

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Section: Annotation Of Existing Cyberbullying Datasetsmentioning

confidence: 99%

Curating Cyberbullying Datasets: a Human-AI Collaborative Approach

Gomez

Sztainberg

Trana

2021

Int Journal of Bullying Prevention

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“…It gives details on cybercrime, the need for social media monitoring as a way of curbing cyber and related crime. More so, [10] created a response grading system that mapped bullying on Twitter to high, medium and low. It pops up a message when it predicts a bully message is about to be sent.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Cyber Bullying on Facebook Using Text Mining

Aliyu

Dogo

Ajibade

et al. 2020

J. Appl. Artif. Intell.

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Cyberbullying is a type of cybercrime that involves the use of the internet and other information technology resources to deliberately insult, embarrass, harass, bully, and threaten people online. The ubiquity of internet connectivity has enabled an increase in the volume and pace of cyberbullying activities because the criminals no longer need to be physically present when committing the crime. This work aims to analyze and predict cyberbullying on Facebook using Naïve Bayes algorithm. The score accuracy, classification report, and confusion matrix are also employed to assess the performance of the classifier. The accuracy of the classifier is 0.95(95%) which means the model can predict 95 of every 100 instances correctly. Also, the result of the experimental analysis shows that Naïve Bayes is effective in classifying a word into a bully or non-bully word and can identify the category of the bully word that is being sent online.

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“…In most studies to date, annotators labeled individual messages instead of message threads, ignoring social context altogether (Al-garadi, Varathan, and Ravana 2016;Huang, Singh, and Atrey 2014;Nahar et al 2014;Reynolds, Kontostathis, and Edwards 2011;Singh, Huang, and Atrey 2016;Sugandhi et al 2016). Only three of the papers that we reviewed incorporated social context in the annotation process.…”

Section: Existing Sources Of Cyberbullying Datamentioning

confidence: 99%

Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification

Ziems

Vigfússon

2020

ICWSM

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Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. We model this behavior using social network and language-based features, which improve classifier performance. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.

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Automatic Monitoring and Prevention of Cyberbullying

Cited by 22 publications

References 4 publications

Curating Cyberbullying Datasets: a Human-AI Collaborative Approach

Curating Cyberbullying Datasets: a Human-AI Collaborative Approach

Analysis of Cyber Bullying on Facebook Using Text Mining

Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification

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