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This study analyzes 25.08 million tweets from seven state-backed influence operations to differentiate between hate speech and other forms of divisive content, revealing that existing metrics overestimate hate by approximately twofold. Using a two-prompt LLM-based detector, the authors classify content into identity-based attacks, partisan attacks, and geopolitical invective, finding that only 18.7% of the content is both identity-based and dehumanizing. The research highlights the complexity of categorizing hostile content and introduces the concept of "manufactured divisiveness," which complicates the understanding of online hate in influence campaigns.
Nearly half of the content labeled as hate in state-backed influence operations is actually partisan or geopolitical invective, challenging the accuracy of current hate speech metrics.
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.