AI-driven recommendation algorithms increasingly shape user experience on social media, raising concerns about transparency, accountability, and user agency. This paper presents a comparative analysis of Explainable AI (XAI) implementations on Facebook, Instagram, TikTok, and Twitter/X. Using a structured framework, we assess two key dimensions: Explanation Adequacy, defined by clarity, specificity, relevance, and verifiability, and Explanation Actionability, defined by proximity, granularity, and reversibility of controls. Our evaluation combines feature audits, cross-platform comparisons, and rubric-based scoring. Results show Facebook provides the strongest balance of adequacy (4/5) and actionability (4/5), Twitter/X offers limited adequacy (2/5) but moderate actionability (3/5), TikTok demonstrates strong actionability (4/5) but generic explanations, and Instagram performs moderately ($3 / 5$on both dimensions). We further extend the analysis by linking adequacy and actionability to perceived usefulness, trust, satisfaction, and algorithmic scepticism. Findings highlight tensions between algorithmic sophistication, user comprehension, and engagement optimization, while also revealing regulatory implications under GDPR and the DSA. This work contributes a standardized evaluation framework for XAI in social computing, empirical evidence of platform disparities, and practical design guidelines for enhancing transparency and user empowerment in recommender systems.

The explosive development of the metaverse as a socio-technical system is a major challenge to national security and governance. The current paper analyses the existing academic and industry literature in order to determine the key risks and its implications. We find that the metaverse is bringing in novel vectors of misinformation and disinformation, radicalisation, financial crime, terrorism, and state-sponsored hybrid warfare, whereas among the most specific dangers are the recruitment of extremists in the virtual realm of immersion, laundering of illicit funds through decentralised economies, identity theft, and critical infrastructure exploitation. The majority of the existing mitigation measures suggested include cybersecurity tools, identity verification, content moderation, and regulatory measures but according to our analysis, coordinated governance, interdisciplinary research, and adaptable policy frameworks are required. The paper offers an in-depth insight into ways of reducing metaverse-associated threats to national security through the combination of technical, social, and policy approaches.