Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations

State-backed influence operations are routinely measured as high-prevalence sources of ``hate'' and ``toxicity.'' We argue those rates rest on a measurement error: the detectors behind them are validated to catch a broader definition inclusive of hostility or divisiveness aimed at an out-group, and so over-attribute hate to content better described as partisan or geopolitical invective. Across 25.08M tweets from seven government-attributed campaigns in the Twitter Information Operations archive (8,275 accounts), we separate hate from the other forms of divisiveness. We first validate a two-prompt LLM-based detector, matching human labels at Cohen's kappa=0.82, to identify the broader hostility; we then develop an auditable rule, agreeing with an expert at kappa=0.52, to further classify this content (5,457 posts) into three sub-categories. About 50.1% are identity-based attacks on people, whereas 30.4% are partisan attacks and 19.5% invective against states and their foreign policy. Reporting all of it as hate therefore overstates hate roughly twofold; only 18.7% is both identity-based and dehumanizing or inciting. Six of seven campaigns sort into three regimes that a single ``hate'' rate flattens, namely identity hate (RU-op and IRA, both Russia-attributed), geopolitical invective (both Iran operations), and partisan divisiveness (both Venezuela operations). We call the shared product manufactured divisiveness. The line to separate these constructs itself remains unsettled: on the hardest cases three independent human experts agree only moderately (pairwise kappa=0.37--0.50), and the best of nineteen LLM models tops out at kappa=0.601 against the experts' majority. Our findings can help redefine the study of hate in the context of influence campaigns and broader online discourse.