News outlets shape public opinion on a scale, which makes automated detection of political bias and factuality essential. Yet, the field still lacks unified resources, comprehensive evaluations in diverse approaches, and systematic analyzes of the representations and fusion strategies that matter the most, especially under label sparsity and dataset diversity. In addition, there is little empirical work that reports broad observation driven findings about what consistently works, what fails, and why. We address these gaps with four contributions: (i) MBFC-2025, a large-scale label set that covers ~2,600 outlets from Media Bias/Fact Check (MBFC); (ii) multi-view representations for ACL-2020 ~900 outlets and MBFC-2025, spanning Alexa graphs, hyperlink graphs, LLM-derived graphs, articles, and Wikipedia descriptions; (iii) systematic evaluation and analysis of embedding views and fusion strategies, including an RL-based fusion variant; and (iv) extensive experiments that achieve state-of-the-art results on ACL-2020 and establish strong benchmarks on MBFC-2025.