Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.

Cyberbullying on social media continues to pose a significant problem for children and young people, and platforms increasingly rely on artificial intelligence (AI) to address it. Nonetheless, little is known about children’s views as to the effectiveness of AI-based interventions; and how these interventions impact their rights to protection, privacy, and freedom of expression, as afforded to them in the United Nations Convention on the Rights of the Child (UNCRC), which applies in a digital environment. This study is the first to ask children about their perceptions as to how AI-based proactive content moderation of cyberbullying impacts their rights. We have designed a set of AI-based interventions into cyberbullying that build on proactive content take-down, based on social learning and social norm theories; we solicited children’s views via focus groups and in-depth interviews as to their perceived effectiveness and impact of such interventions on children’s rights (children from Ireland, age 12–17, N = 59). We discuss how youth social norms can limit the effectiveness of the interventions and the need to involve youth in platform decisions regarding AI design.

This brief article serves as an introductory piece for the special issue “The Use of Artificial Intelligence (AI) to Address Online Bullying and Abuse.” It provides an overview of the state of the art with respect to the use of AI in addressing various types of online abuse and cyberbullying; current challenges for the field; and it emphasises the need for greater interdisciplinary collaboration on this topic. The article also summarises key contributions of the articles selected for the special issue.