Skip to content
Review Open access

Five Myths About Influence Operations: What 25 Million Tweets Across Seven State Campaigns Reveal

Aug 2026 · Systems · Vol 14, pp. 970 · 1 citation · 62 references

Abstract

A set of “stylized facts” about state-backed influence operations now circulates across journalism, policy, and the peer-reviewed literature: that they are monolithic troll armies; that they win by weaponizing moral-emotional language; that they manufacture their own virality; that they learn and optimize against feedback; and that they have become indistinguishable from ordinary users. Most rest on single-campaign studies, uncontrolled comparisons, and large-sample significance reported without baselines or multiple-comparison control, and are rarely re-tested. We assemble complete, government-attributed archives of seven state campaigns (25,076,853 tweets from 9071 accounts) with a matched organic-user baseline for five of them, and re-test all five claims under one protocol: pre-registration, Benjamini–Hochberg false-discovery control, permutation nulls, a future-reception placebo, and a takedown-snapshot decomposition. Scoped to these campaigns, every claim weakens or reverses: the operations are narratively segregated, thinly staffed production desks, not a unified army; an organic moral-contagion law fails to replicate in any of them, and a meaningless placebo predicts engagement as well; internal amplification supplies only 0.10–5.31% of top-percentile reach; the remainder is captured from an external audience whose composition—genuine organic uptake versus coordination the archive cannot see—is structurally unobservable in takedown data, a limit we state as part of the finding; behavior is scripted, with rare apparent feedback mean-reverting toward baseline; and, while their per-account language has drifted off the 2016 “troll” fingerprint, they still coordinate 7–70× more tightly than matched real users—a regularity a frozen re-test reproduces, with the language-drift and segregation patterns, across twelve further country-groups. The five verdicts do not carry equal evidentiary weight: three rest on matched-baseline contrasts, one on a placebo-gated temporal design, and one (the moral-emotional claim) on a placebo-anchored test alone, a hierarchy this paper makes explicit. The corrected picture is coherent: an industrial content factory siloed in production but coordinated in execution, whose reach it does not internally manufacture. The detectable signature has migrated from language to coordination: per-account content fingerprints age out, while cross-account coordination remains the durable, cross-national marker. Because these operations run scripts rather than optimize against feedback, an adversary that genuinely optimized—now feasible with large language models—would look measurably different from the operations studied here: a forward warning, not a present finding. The recurring lesson is methodological: on corpora of confirmed manipulation, baseline-free significance reconstructs the analyst’s expectations, and platform and policy decisions rest on those beliefs.

Read PDF

Similar papers

Review Open access Sep 2026

Political memes and influence in the Global South: Calibrated prediction of misinformation and opinion change

Memes have become a routine infrastructure of political talk across platforms, blending humor, affect, and compressed commentary. Yet scholarship often alternates between strong causal claims and descriptive accounts, leaving open what can be predicted—reliably and with well-calibrated probabilities—from lightweight su...

Carlos Humberto Hidalgo Menjívar, Carlos Alberto Echeverría Mayorga · 0 citations
Preprint Aug 2026

FUBU-EPSTEIN: A Large-Scale Twitter Dataset on the Jeffrey Epstein Case and Its Global Public Discourse (2019-2023)

The criminal case of Jeffrey Epstein has generated a complex, long-running global discourse on digital platforms, characterized by punctuated attention shocks, conspiracy theories, and blame attribution. To facilitate the computational study of these dynamics, we introduce the FUBU-EPSTEIN dataset, a large-scale, multi...

Michael Kreil, Tristan Manfred Stöber, D. Schroeder · 0 citations
Open access Sep 2026

Comparative Sentiment Analysis of Campaign and Third-Party Communications in the 2024 U.S. Elections

In an era defined by constant digital interaction, personal information is frequently collected, shared, and repurposed in ways that raise serious concerns about privacy and transparency. As part of a broader project, which investigates how individuals’ personal information is used, an experiment was designed to test h...

Christopher Henshaw, Jared Byers, Alan J. Michaels · 1 citation
Open access 2026

The Valence Paradox: Positive Policy, Political Outrage, and Engagement on X in the 2024 U.S. Presidential Election

This study engages in a machine learning analysis of the relationship between valence under digital messages (positive vs. negative messages), engagement actions (likes and retweets), over the performance of presidential election contenders during the United States Presidential Election-2024 on Twitter/X. Using Natural...

Vedant Pandey · 0 citations
#artificial intelligence Preprint Aug 2026

How Identity and Opinion Shape Political Sycophancy in LLMs

A framework that disentangles two distinct triggers of political sycophancy: opinion (aligning with explicit narratives) and identity (stereotyping based on demographic labels) is introduced, highlighting how personalization may amplify identity- or opinion-conditioned shifts in the model's behaviors.

Li-Ni Fu, Chang-Chih Meng, Chien-Hua Chen et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.