Script-based story matching for cyberbullying prevention

Macbeth, Jamie; Adeyema, Hanna; Lieberman, Henry; Fry, Christopher

doi:10.1145/2468356.2468517

Cited by 14 publications

(11 citation statements)

References 5 publications

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“…This led to the removal of a further 18 papers. These included papers that dealt with themes such as youth violence involvement detection (Sigel and Harpin, 2013), story matching to identify distressed teens (Dinakar et al, 2012b;Macbeth et al, 2013), and cyberbully prevention policies (Al Mazari, 2013). To eliminate the effects of language on cyberbully detection when comparing the reviewed studies, we excluded papers using non-English corpora; thus a further 7 papers were excluded.…”

Section: Data Search and Selectionmentioning

confidence: 99%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

153

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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: Data Search and Selectionmentioning

confidence: 99%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

153

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“…Researchers (Macbeth et al, 2013; have reported that cyberbullying posts are contextual, personalized and creative, which make them harder to detect than detecting spam. Even without using bad words, the post can be hostile to the receiver.…”

Section: Related Researchmentioning

confidence: 99%

Proceedings of the First Workshop on Abusive Language Online

Waseem¹,

Chung²,

Hovy³

et al. 2017

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ISBN 978-1-945626-66-1 iii IntroductionWe are very pleased to welcome you to the first Workshop on Abusive Language Online (ALW), held at ACL 2017 in Vancouver, Canada. The last few years have seen a surge in abusive behavior online, with governments, social media platforms, and individuals struggling to cope with the consequences and to produce effective methods to combat it. In many cases, online forums, comment sections, and social media interactions have become sites for bullying, scapegoating, and hate speech. These forms of online aggression not only poison the social climate of the online communities that experience it, but can also provoke physical violence and harm.Addressing abusive language necessitates a multidisciplinary approach that requires knowledge from several fields, including, but not limited to: media studies, natural language processing (NLP), psychology, sociology, law, gender studies, communications, and critical race theory. NLP, as a field that directly works with computationally analyzing language, is in a unique position to develop automated methods to analyse, detect, and filter abusive language. By working across disciplinary divides, researchers in all these fields can produce a comprehensive approach to abusive language that blends together computational, social and legal methods.We are therefore very happy to bring researchers of various disciplines together in this one-day workshop to discuss approaches to abusive language. The workshop consists of two invited speaker talks, two panels, and oral and poster presentations. • Carol ToddCarol Todd founded the Amanda Todd Legacy in memory of her daughter Amanda after her death by suicide on October 10, 2012. Amanda's Legacy was created to bring increased awareness and conversations within families and communities about online exploitation, cyberabuse and internet safety. The goal has been to encourage a shift in thinking about bullying type behaviours (both on and offline) to those of REFLECTION and RESPECT as well as to destigmatizing the perceptions related mental health as it can relate to how we treat others.• Brianna Wu Brianna Wu is a 2018 Democratic candidate for U.S. Congress in Massachusetts-District 8. Brianna is also head of development at GSX, a Boston independent videogame studio. Brianna came to national prominence when she and other women working in the tech industry were personally targeted by alt-right hate groups, including one spearheaded by Steve Bannon, now Chief Strategist to Donald Trump. Despite threats on her life and her family, Brianna has never wavered as a voice for the marginalized, including women, people of color and LGBT individuals.We will be hosting the following researchers as our panelists: • Lucas DixonLucas Dixon is Chief Scientist at Jigsaw, an incubator within Alphabet that builds technology to tackle iv some of the toughest global security challenges facing the world today. His work focuses on security, machine intelligence and data visualization.. • Pascale FungPascale Fung is a Professor a...

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“…These studies are separated into two groups according to the intended cyber-bullying. The first group targets direct cyber-bullying, where assailants attack the victim directly by negative and offensive messages [2][3][6][7][8].…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [6] and [7] apply social and psychological methods to examine the negative effects on adolescents. Few consider the innovative design of social network software as a tool for mitigating this problem.…”

Section: Related Workmentioning

confidence: 99%

A Study of Contact Network Generation for Cyber-bullying Detection

Tagami

2014

2014 28th International Conference on Advanced Information Networking and Applications Workshops

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Cyber-bullying is widely recognized as a serious social problem, especially for adolescents and it is also becoming a threat to the viability of online social networks. Several researchers have proposed digital-communication based frameworks whose goal is to help detect bullying in social networks. In this paper, our study focuses on detecting relation-based cyber-bullying, which is an indirect attack on a human, e.g., isolating a victim by ignoring the victim's messages. Recently, relationship-based cyber-bullying has received attention as a new type of cyber-bullying, and detecting it is still a novel problem. As it attacks a human relationship, the detection should monitor the change of the human relationship. In this paper, for the first step of relation-based cyber-bullying detection, we propose a framework to generate a contact network. The framework consists of two phases for a reduction of false negative, i.e. students are friends in the school but detected as non-friend in the Social Networking Service (SNS), which is a serious problem for the cyber-bullying detection. Finally, this paper analyzes the collected SNS data with the actual human relationships, and evaluates the proposed framework.

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Script-based story matching for cyberbullying prevention

Cited by 14 publications

References 5 publications

Approaches to Automated Detection of Cyberbullying: A Survey

Approaches to Automated Detection of Cyberbullying: A Survey

Proceedings of the First Workshop on Abusive Language Online

A Study of Contact Network Generation for Cyber-bullying Detection

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