Identification and characterization of cyberbullying dynamics in an online social network

Squicciarini, Anna; Rajtmajer, Sarah; Liu, Y.; Griffin, Christopher

doi:10.1145/2808797.2809398

Cited by 73 publications

(56 citation statements)

References 11 publications

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“…Determining the severity of cyberbullying by computing a score indicative of the bullying severity of messages and/or sender is performed by studies such as Chen et al Squicciarini et al (2015) were the only studies we found that proposed the relatively novel task of detecting and classifying the events that occur after a cyberbullying incident.…”

Section: Dimensions Of Characterizationmentioning

confidence: 99%

“…Huang et al (2014) discovered that the risk of cyberbullying is decreased in ego networks with many people and high interconnectivity (probably because in such networks there is likely to be increased social support for potential victims) but that a higher number of messages exchanged between users indicate a higher likelihood for cyberbullying. In Squicciarini et al (2015), the authors used the elapsed time between comments to measure the influence of cyberbullies on other users and the proliferation of bullying across a social network. Followers' numbers on social networks were used as features by both Rafiq et al (2015) and Hosseinmardi et al (2015) with Rafiq et al (2015) supplementing this with other network-based features such as media uploads, likes, comments and views.…”

Section: Network-based Featuresmentioning

confidence: 99%

“…A system such as this can benefit from Parime and Suri's (2014) proposal for a dynamically-sourced profane wordlist that is regularly updated from online resources to ensure that new offensive words are captured as they are coined. Squicciarini et al (2015) used personal, social network and content-specific features with a C4.5 Decision Tree classifier to detect bullies on online social networks such as MySpace and spring.me, and devised a set of rules to determine if a user's cyberbullying behaviour is instigated by the actions of another bully. Similarly, Huang et al (2014) found that including social features mined from a user's ego networks as input features to J48, Naïve Bayes, SVM, and ZeroR classifiers improved cyberbullying detection over the use of textual features alone.…”

Section: Supervised Learning Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

150

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Abstract-Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field.

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Section: Dimensions Of Characterizationmentioning

confidence: 99%

Section: Network-based Featuresmentioning

confidence: 99%

Section: Supervised Learning Approachesmentioning

confidence: 99%

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Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

150

View full text Add to dashboard Cite

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“…On the other hand, the papers [17] [18] [19] propose that for the detection it is necessary to take into account the context as well as the profile and characteristics of the users being studied. The obtained results reflect an improvement in the accuracy for the detection of cyberbullying.…”

Section: Related Workmentioning

confidence: 99%

Application Based on Fuzzy Logic to Detect and Prevent Cyberbullying Through Smartphones

Concepción-Sánchez¹,

Caballero‐Gil²,

Molina-Gil³

2017

Computer Science &Amp; Information Technology

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“…Since cyberbullying is not restricted by time and place, it has more insidious effects than traditional forms of bullying Squicciarini et al (2015).…”

mentioning

confidence: 99%

Sentiment Informed Cyberbullying Detection in Social Media

Dani

Liu

2017

Machine Learning and Knowledge Discovery in Databases

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Cyberbullying is a phenomenon which negatively affects individuals. Victims of the cyberbullying suffer from a range of mental issues, ranging from depression to low selfesteem. Due to the advent of the social media platforms, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include use of standards and guidelines, human moderators, use of blacklists based on profane words, and regular expressions to manually detect cyberbullying. However, these mechanisms fall short in social media and do not scale well. Users in social media use intentional evasive expressions like, obfuscation of abusive words, which necessitates the development of a sophisticated learning framework to automatically detect new cyberbullying behaviors. Cyberbullying detection in social media is a challenging task due to short, noisy and unstructured content and intentional obfuscation of the abusive words or phrases by social media users. Motivated by sociological and psychological findings on bullying behavior and its correlation with emotions, we propose to leverage the sentiment information to accurately detect cyberbullying behavior in social media by proposing an effective optimization framework. Experimental results on two realworld social media datasets show the superiority of the proposed framework. Further studies validate the effectiveness of leveraging sentiment information for cyberbullying detection.

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Identification and characterization of cyberbullying dynamics in an online social network

Cited by 73 publications

References 11 publications

Approaches to Automated Detection of Cyberbullying: A Survey

Approaches to Automated Detection of Cyberbullying: A Survey

Application Based on Fuzzy Logic to Detect and Prevent Cyberbullying Through Smartphones

Sentiment Informed Cyberbullying Detection in Social Media

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