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What the evidence suggests
Researchers and users point out repetitive phrasing (e.g., warnings that “the left” will pay, “this is war,” or "you have no idea what is coming") appearing in many posts within a narrow timeframe. Many of these posts come from low-engagement accounts with default or generic profiles.
"In the wake of the assassination of Charlie Kirk, we are going to see a lot of accounts pushing, effectively, for civil war in the U.S. This includes the rage-baiter-in-chief, Elon Musk, but also an army of Russian and Chinese bots and their faithful shills in the West," wrote University of San Diego political science professor Branislav Slantchev on X.
In the wake of the assassination of Charlie Kirk, we are going to see a lot of accounts pushing, effectively, for civil war in the US. This includes the rage-baiter in chief, Elon Musk, but also an army of Russian and Chinese bots and their faithful shills in the West.
Do not… https://t.co/OyErwAYnV8
— Branislav Slantchev (@slantchev) September 10, 2025
He cited a viral thread of X posts from purported bot users that advocated for retributive violence. The poster claimed that "half of them have an AI-generated profile photo, the standard bio schlop, and the standard banners."
Such patterns—rapid appearance of similar content across many accounts—are consistent with known botnet coordination or message amplification. While these are based on user observations more than systematic data to date, the consistency with known bot behavior adds weight to suspicions.
Past research provides a baseline for what bot-amplified political content looks like on X (formerly Twitter). A Plos One study in February found that after Elon Musk’s acquisition of the platform in late 2022, hate speech increased and there was no reduction in activity of inauthentic or “bot-like” accounts.
Another investigation by Global Witness last summer uncovered a small set of bot-like accounts (45 accounts in one instance) that between them generated over 4 billion impressions for partisan, conspiratorial, or abusive content. This type of amplification shows the potential reach of such networks.
Finally, there is a history of states or organized groups deploying botnets or troll farms to exploit US political polarization. Examples include Russia’s Doppelgänger campaign, “Spamouflage” (Chinese government-linked), and others that have mimicked US users, used AI-generated or manipulated content, or pushed divisive rhetoric for political leverage.
Nothing definitive yet
As of now, no credible cybersecurity firm, government agency, or academic group has publicly attributed a bot network—foreign or domestic—with high confidence to the wave of “civil war” rhetoric following Kirk’s death.
The MAGA terrorist bots are honouring Charlie Kirk by sending death threats to anyone they perceive to be “left” or a “democrat”, Including public figures. This is likely part of a coordinated Russian campaign to spread chaos and create political unrest, be aware, stay alert.
— Anonymous (@YourAnonCentral) September 10, 2025
It is also not clear how many of the posts are automated vs. organic (real users). The portion coming from apparently bot-like accounts vs the broader public discourse is unknown. Also, it’s not established whether any such amplification has a top-down command structure (i.e. centrally coordinated) or is more ad-hoc.
And X is rife with plenty of verified influencers on the right calling for civil war or violent attacks on the left.
Nonetheless, when the U.S. suffers a national tragedy like yesterday's shooting, groups with a record of exploiting political polarization have seized on the opportunity. Russia’s bot farms (e.g. Internet Research Agency/“Storm”-type operations) have long been flagged. Chinese-linked disinformation networks (e.g. “Spamouflage”) are documented to have used social media amplification and content farming to influence U.S. public sentiment.
And the rise of AI-enabled content generation makes it easier for bot networks to produce plausible, human-like posts at scale. Research shows that bot detection is increasingly challenged by accounts that mimic human language, timing, and variation. A recent bot detection review found evolving concealment techniques and gaps in current detection methods.
