Large language models (LLMs) are used as agents in social-media simulations, where behavioral fidelity may depend on preserving the affective intensity of real discourse. Using 3,692 unique Bluesky posts expanded into 14,768 activation records, we extract mild–strong affect directions in Qwen2.5 and Llama-3.1 Base and instruction-tuned checkpoints. Measured separation and reciprocal transfer to non-political hostility support a broader hostility-related feature rather than a uniquely partisan axis. In a six-prompt steering probe, checkpoints differ in their labeled hostile expression, refusal/deflection, and neutralization. Refusal/deflection includes substantive answers with disclaimers. Same-layer comparisons distinguish the hostility directions from a measured harmful-request contrast; removing that component changes behavioral counts non-uniformly and does not consistently return them to baseline. These descriptive results illustrate a representation–expression gap and motivate downstream fidelity audits.