A review on deep-learning-based cyberbullying detection

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented

Algorithmic Reason

Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences

Algorithmic Reason

Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences