Enhancing the Identification of Cyberbullying through Participant Roles

Rathnayake, Gathika; Atapattu, Thushari; Herath, Mahen; Zhang, Georgia; Falkner, Katrina

doi:10.18653/v1/2020.alw-1.11

Cited by 9 publications

(11 citation statements)

References 11 publications

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“…Ratnayaka et al [24] implemented DistilBERT in identifying cyberbullying detection through role modelling. Ask.fm dataset was utilized to categorize the participant roles into victim and harasser, which is a multi-class classification problem.…”

Section: Model C: Training Data Was Balancedmentioning

confidence: 99%

Cyberbullying Detection using Ensemble Method

Saranyanath¹,

Shi²,

Corriveau³

2022

Data Science and Machine Learning

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Cyberbullying is a form of bullying that occurs across social media platforms using electronic messages. This paper proposes three approaches and five models to identify cyberbullying on a generated social media dataset derived from multiple online platforms. Our initial approach consists in enhancing Support Vector Machines. Our second approach is based on DistilBERT, a lighter and faster Transformer model than BERT. Staking the first three models we obtain two more ensemble models. Contrasting the ensemble models with the three others, we observe that the ensemble models outperform the base model concerning all evaluation metrics except precision. While the highest accuracy, of 89.6%, was obtained using an ensemble model, we achieved the lowest accuracy, at 85.53% on the SVM model. The DistilBERT model exhibited the highest precision, at 91.17%. The model developed using the different granularity of features outperformed the simple TF-IDF.

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Section: Model C: Training Data Was Balancedmentioning

confidence: 99%

Cyberbullying Detection using Ensemble Method

Saranyanath¹,

Shi²,

Corriveau³

2022

Data Science and Machine Learning

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“…The dataset's effectiveness in training classifiers may, however, be affected by the low percentage of abusive documents present. This dataset was subsequently re-annotated by Rathnayake et al (2020) to identify which of the four roles of 'harasser', 'victim', 'bystander defender' and 'bystander assistant' was played by the authors of the posts contained in the dataset. Similarly, used the same four roles to annotate a dataset created from simulated cyberbullying episodes using the instant messaging tool; WhatsApp, along with the labels created by Hee et al ( 2018) used a hierarchical annotation scheme that, in addition to identifying offensive tweets, also identifies if such tweets are targeted at specific individuals or groups and what type of target it is (i.e., individual -@username or group -'.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

2021

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Message from the OrganisersDigital technologies have brought myriad benefits for society, transforming how people connect, communicate and interact with each other. However, they have also enabled harmful and abusive behaviours to reach large audiences and for their negative effects to be amplified, including interpersonal aggression, bullying and hate speech. Already marginalised and vulnerable communities are often disproportionately at risk of receiving such abuse, compounding other social inequalities and injustices. The Workshop on Online Abuse and Harms (WOAH) convenes research into these issues, particularly work that develops, interrogates and applies computational methods for detecting, classifying and modelling online abuse.Technical disciplines such as machine learning and natural language processing (NLP) have made substantial advances in creating more powerful technologies to stop online abuse. Yet a growing body of work shows the limitations of many automated detection systems for tackling abusive online content, which can be biased, brittle, low performing and simplistic. These issues are magnified by the lack of explainability and transparency. And although WOAH is collocated with ACL and many of our papers are rooted firmly in the field of machine learning, these are not purely engineering challenges, but raise fundamental social questions of fairness and harm. For this reason, we continue to emphasise the need for inter-, cross-and anti-disciplinary work by inviting contributions from a range of fields, including but not limited to: NLP, machine learning, computational social sciences, law, politics, psychology, network analysis, sociology and cultural studies. In this fifth edition of WOAH we direct the conversation at the workshop through our theme: Social Bias and Unfairness in Online Abuse Detection Systems. Continuing the tradition started in WOAH 4, we have invited civil society, in particular individuals and organisations working with women and marginalised communities, to submit reports, case studies, findings, data, and to record their lived experiences through our civil society track. Our hope is that WOAH provides a platform to facilitate the interdisciplinary conversations and collaborations that are needed to effectively and ethically address online abuse.Speaking to the complex nature of the issue of online abuse, we are pleased to invite Leon Derczynski, currently an Associate Professor at ITU Copenhagen who works on a range of topics in Natural Language Processing; Deb Raji, currently a Research Fellow at Mozilla who researches AI accountability and auditing; Murali Shanmugavelan, currently a researcher at the Centre for Global Media and Communications at SOAS (London) to deliver keynotes. We are grateful to all our speakers for being available, and look forward to the dialogues that they will generate. On the day of WOAH the invited keynote speakers will give talks and then take part in a multi-disciplinary panel discussion to debate our theme and other issues in computational o...

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“…The dataset's effectiveness in training classifiers may, however, be affected by the low percentage of abusive documents present. This dataset was subsequently re-annotated by Rathnayake et al (2020) to identify which of the four roles of 'harasser', 'victim', 'bystander defender' and 'bystander assistant' was played by the authors of the posts contained in the dataset. Similarly, Sprugnoli et al (2018) used the same four roles to annotate a dataset created from simulated cyberbullying episodes using the instant messaging tool; WhatsApp, along with the labels created by Hee et al ( 2018) Zampieri et al (2019) used a hierarchical annotation scheme that, in addition to identifying offensive tweets, also identifies if such tweets are targeted at specific individuals or groups and what type of target it is (i.e., individual -@username or group -'.…”

Section: Related Workmentioning

confidence: 99%

A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection

Salawu¹,

Lumsden²,

He³

2021

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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In this paper, we introduce a new English Twitter-based dataset for online abuse and cyberbullying detection. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insult, profanity, sarcasm, threat, porn and exclusion. Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes. The dataset was used to train a number of transformer-based deep learning models returning impressive results.

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Enhancing the Identification of Cyberbullying through Participant Roles

Cited by 9 publications

References 11 publications

Cyberbullying Detection using Ensemble Method

Cyberbullying Detection using Ensemble Method

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection

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