The manifestation of cyberbullying (CB) on social media platforms (SMP) evolves alongside advancements in Internet communication technologies (ICT).Current automated CB detection techniques, often relying on artificial intelligence (AI) are overly simplistic, overlooking the multimodal nature of user-generated content and the relational nature of CB and treating it as a binary problem.Addressing these limitations, this mixed methods study involves young people (aged 13-17) in developing CB detection software following the principles of the Software Development Life Cycle (SDLC).To explore the perspectives of young people on online harms related to CB across various SMPs, we first surveyed 104 participants aged 13-17 in Ireland.Following the call of the European Union Joint Research Center (EU JRC) to integrate young people's perspectives into the design of AI systems that serve their needs, we facilitate co-design sessions with "junior researchers" (aged 15-16) during a week-long research internship.In these sessions, participants adapt existing CB scenarios to reflect the realities of today's SMP and ICT environments, ensuring the outcomes are relevant and youth-informed.Additionally, the junior researchers help craft more than 550 text messages 1 across multiple cyberbullying scenarios that reflect bystander-enabler and defender behaviours (e.g., escalate or de-escalate the cyberbullying behaviour).Tailored to reflect the dynamics of the comment-thread and mulit-party conversations, these messages are current under review by experts.This structured and generative input positions them from users to co-designers of a youth-informed benchmark dataset that will support the evaluation of future CB detection systems.

Image-based sexual abuse (IBSA) refers to the nonconsensual creating, taking, or sharing of intimate images, including threats to share intimate images.Despite the signifcant harms of IBSA, there is limited data on its prevalence and how it afects diferent identity or demographic groups.This study examines prevalence of, impacts from, and responses to IBSA via a survey with over 16,000 adults in 10 countries.More than 1 in 5 (22.6%) respondents reported an experience of IBSA.Victimization rates were higher among LGBTQ+ and younger respondents.Although victimized at similar rates, women reported greater harms and negative impacts from IBSA than men.Nearly a third (30.9%) of victim-survivors did not report or disclose their experience to anyone.We provide large-scale, granular, baseline data on prevalence in a diverse set of countries to aid in the development of efective interventions that address the experiences and intersectional identities of victim-survivors. CCS Concepts• Human-centered computing → Empirical studies in collaborative and social computing.

Information is crucial to how people understand their mental health and well-being, and many turn to online sources found through search engines and social media. We present an interview study (n = 17) of participants who use online platforms to seek information about their mental illnesses. Participants use their personal information ecosystems in a cyclical process to find information. This cycle is driven by the adoption of new information and questioning the credibility of information. Privacy concerns fueled by perceptions of stigma and platform design also influence their information-seeking decisions. Our work proposes theoretical implications for social computing and information retrieval on information seeking in users’ personal information ecosystems. We offer design implications to support users in navigating personal information ecosystems to find mental health information.

Deepfake technologies have become ubiquitous, “democratizing” the ability to manipulate photos and videos. One popular use of deepfake technology is the creation of sexually explicit content, which can then be posted and shared widely on the internet. Drawing on a survey of over 16,000 respondents in 10 different countries, this article examines attitudes and behaviors related to “deepfake pornography” as a specific form of non-consensual synthetic intimate imagery (NSII). Our study found that deepfake pornography behaviors were considered harmful by respondents, despite nascent societal awareness. Regarding the prevalence of deepfake pornography victimization and perpetration, 2.2% of all respondents indicated personal victimization, and 1.8% all of respondents indicated perpetration behaviors. Respondents from countries with specific legislation still reported perpetration and victimization experiences, suggesting NSII laws are inadequate to deter perpetration. Approaches to prevent and reduce harms may include digital literacy education, as well as enforced platform policies, practices, and tools which better detect, prevent, and respond to NSII content.

In the digital age, cyberbullying (CB) poses a significant concern, impacting individuals as early as primary school and leading to severe or lasting consequences, including an increased risk of self-harm. CB incidents, are not limited to bullies and victims, but include bystanders with various roles, and usually have numerous sub-categories and variations of online harms. This position paper emphasises the complexity of CB incidents by drawing on insights from psychology, social sciences, and computational linguistics. While awareness of CB complexities is growing, existing computational techniques tend to oversimplify CB as a binary classification task, often relying on training datasets that capture peripheries of CB behaviours. Inconsistent definitions and categories of CB-related online harms across various platforms further complicates the issue. Ethical concerns arise when CB research involves children to role-play CB incidents to curate datasets. Through multi-disciplinary collaboration, we propose strategies for consideration when developing CB detection systems. We present our position on leveraging large language models (LLMs) such as Claude-2 and Llama2-Chat as an alternative approach to generate CB-related role-playing datasets. Our goal is to assist researchers, policymakers, and online platforms in making informed decisions regarding the automation of CB incident detection and intervention. By addressing these complexities, our research contributes to a more nuanced and effective approach to combating CB especially in young people.