Moderating online child sexual abuse material (CSAM): Does self-regulation work, or is greater state regulation needed?

Abstract

Social media platforms are crucial public forums connecting users around the world through a decentralised cyberspace. These platforms host high volumes of content and, as such, employ content moderators (CMs) to safeguard users against harmful content like child sexual abuse material (CSAM). These roles are critical in the social media landscape however, CMs’ work as “digital first responders” is complicated by legal and systemic debates over whether the policing of cyberspace should be left to the self-regulation of technology companies, or if greater state-regulation is required. In this empirical policy and literature review, major debates in the area of content moderation and, in particular, the online policing of CSAM are identified and evaluated. This includes the issue of territorial jurisdiction, and how it obstructs traditional policing; concerns over free speech and privacy if CMs are given greater powers, and debates over whether technology companies should be legally liable for user-generated content (UGC). In outlining these issues, a more comprehensive foundation for evaluating current practices for monitoring and combatting online CSAM is established which illustrates both the practical and philosophical challenges of the existing status quo, wherein the state and private companies share these important responsibilities.

Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence

“…In order to verify the effectiveness of the algorithm, combined with the online learning self-monitoring strategy, this section adopts the questionnaire on the online learning self-monitoring ability of college students, by comparing the Likert-type scores of the five strategy elements in the online learning ability questionnaire of college students before and after the use of self-monitoring strategy [28], establishing the score difference objective function, and constructing the fusion optimization model, the The CatLevyNRO algorithm was utilized to solve the fusion weights of the students in the questionnaire.…”