A multi-platform dataset for detecting cyberbullying in social media

Bruwaene, David Van; Huang, Qianjia; Inkpen, Diana

doi:10.1007/s10579-020-09488-3

Cited by 44 publications

(34 citation statements)

References 11 publications

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“…The study in Van Bruwaene et al (2020) formed a dataset using multiple platforms in English language from SafeToNet's VISR-branded child safety app for adolescents. In collaboration with expert annotators, they utilized crowd sourcing and machine learning techniques to enlarge the corpus and handle skew in iterative manner.…”

Section: Related Workmentioning

confidence: 99%

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Dewani¹,

Memon²,

Bhatti³

2021

3C TIC

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Automatic Cyberbullying detection has remained very challenging task since social media content and conversations are usually posted in unstructured free-text form leaving behind the language norms. The major concern and gap in formulating cyberbullying detection strategies is scarcity of available linguistic resources typically for newly evolved languages. Roman Urdu has recently emerged and hence is a resource poor language. Urdu has been widely known as the national language of Pakistan. However, because of socio-cultural and multilingual aspects, Roman Urdu is used widely on the Internet by Asians and more specifically Pakistanis.To fulfil the above stated gap, this research work presents guidelines for data annotation process and developed two linguistic resources: (i) Annotated corpus in Roman Urdu Language for cyberaggression and offensive language detection. The process of data annotation involved bilingual annotators instead of crowdsourcing. It has the benefit of correctly annotating instances that constitute clear cases of cyberbullying without compromising data quality. The developed corpus is highly balanced (with almost negligible skew) unlike most of the existing corpuses even in mature languages. (ii) Processing textual information for NLP tasks involves Stop-word elimination as a sub phase. Stop words carry least semantic information and increase feature space as compared to the other tokens and index terms in corpora. We have developed domain specific stop words for Roman Urdu Language considering all the lexical variants and typically in the context of aggression detection and collected data. The work has been carried out using python programming language and Pycharm IDE.

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Section: Related Workmentioning

confidence: 99%

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Dewani¹,

Memon²,

Bhatti³

2021

3C TIC

View full text Add to dashboard Cite

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“…Data can be collected from a single platform, such as Yahoo! (Djuric et al, 2015), Wikipedia (Wulczyn et al, 2017), Facebook (Kumar et al, 2018), Twitter (Waseem & Hovy, 2016;Davidson et al, 2017;Founta et al, 2018), or from multiple discussion forums (Van Bruwaene et al, 2020). Sigurbergsson and Derczynski (2020) demonstrated that although language and user behaviour vary between platforms, sharing information across languages and platforms improves the performance of automatic systems.…”

Section: Annotated Datasetsmentioning

confidence: 99%

Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective

Kiritchenko

Nejadgholi

Fraser

2021

jair

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The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm. Significant effort in Natural Language Processing (NLP) research has been devoted to addressing this problem through abusive content detection and related sub-areas, such as the detection of hate speech, toxicity, cyberbullying, etc. Although current technologies achieve high classification performance in research studies, it has been observed that the real-life application of this technology can cause unintended harms, such as the silencing of under-represented groups. We review a large body of NLP research on automatic abuse detection with a new focus on ethical challenges, organized around eight established ethical principles: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. In many cases, these principles relate not only to situational ethical codes, which may be context-dependent, but are in fact connected to universal human rights, such as the right to privacy, freedom from discrimination, and freedom of expression. We highlight the need to examine the broad social impacts of this technology, and to bring ethical and human rights considerations to every stage of the application life-cycle, from task formulation and dataset design, to model training and evaluation, to application deployment. Guided by these principles, we identify several opportunities for rights-respecting, socio-technical solutions to detect and confront online abuse, including ‘nudging’, ‘quarantining’, value sensitive design, counter-narratives, style transfer, and AI-driven public education applications.evaluation, to application deployment. Guided by these principles, we identify several opportunities for rights-respecting, socio-technical solutions to detect and confront online abuse, including 'nudging', 'quarantining', value sensitive design, counter-narratives, style transfer, and AI-driven public education applications.

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“…Yuvaraj et al [46] proposed a framework integrating artificial neural network (ANN) and deep reinforcement learning (DRL) and achieved an accuracy of 98% on the Twitter data (30, Most of the research mentioned above focuses on detecting cyberbullying on datasets collected from one or two social media platforms. Bruwaene et al [78] collected a text-based dataset from VISR tool of SafeToNet that monitors the social media activity of a child on various social media platforms. They received an F-Score of 0.885 to classify bullying and non-bullying using CNN.…”

Section: Text-based Cyberbullying Detectionmentioning

confidence: 99%

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Arif

2021

JISCR

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Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

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A multi-platform dataset for detecting cyberbullying in social media

Cited by 44 publications

References 11 publications

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

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