Memetic Wars: how a meme war is orchestrated, measured and shut down

A diplomat versus a pack of Shiba Inus

June 2022. Mikhail Ulyanov, Russia's permanent representative to the international organisations in Vienna, replies on Twitter to an account whose avatar is a Shiba Inu wearing a military patch. He writes a clumsy line: "You pronounced this nonsense. Not me". Within hours, hundreds of accounts with the same dog, each with its own twist, turn the phrase into a meme and chase it through every one of his replies. Vice ran the story under a blunt headline: the Shiba Inu accounts "chased a Russian diplomat offline".

Those accounts were NAFO, the North Atlantic Fella Organization. It isn't an organisation; it's a swarm. It was born on 24 May 2022 from a drawing that a Pole, Kamil Dyszewski, gave to anyone who donated to the Georgian Legion fighting in Ukraine. By August, Ukraine's Ministry of Defence was thanking it publicly and Defence Minister Reznikov had switched his avatar to a dog. In 2023, Lithuania decorated its founder.

Six years earlier, in a building in St Petersburg, more than eighty people worked on a "translator project" aimed at the United States, inside an operation with a monthly budget of more than 73 million roubles (over $1.25 million). Its signature format was the meme too.

These are two opposite ways of doing the same thing: fighting a war on a front that doesn't appear on any map. This piece is about that front.

The question, in four parts

"Do memes work as weapons?" is too big to answer in one go. We break it into four:

1. What exactly is memetic warfare, and who has written it down? Doctrine, not folklore.
2. How is it orchestrated? Which structures, budgets and formats show up in the documented cases.
3. How do you measure whether it worked? Because volume isn't impact, and that's the main surprise here.
4. How is it managed and shut down? What the evidence says about detecting, containing, debunking and taking down.

One warning before we start: this is analysis, not a manual. We describe how the documented campaigns were organised and how they are detected and dismantled. We don't explain how to build a deception operation, because that isn't research.

What memetic warfare is

The word meme was coined by Richard Dawkins in The Selfish Gene (1976), in the chapter "Memes: the new replicators". He shortened the Greek mimeme, "imitated thing", so it would rhyme with gene. His examples were tunes, catch-phrases, fashions and ways of building arches: units of culture that get copied, mutate and compete for attention the way genes compete to survive. The internet only changed the speed.

The jump into military writing came early and, at first, from the fringes:

• 2005–2006. Major Michael B. Prosser of the US Marine Corps proposes, in "Memetics — A Growth Industry in US Military Operations", a Meme Warfare Center with a "Meme Management Officer" advising the commander on meme "generation and transmission".
• 2010. Lieutenant Brian J. Hancock publishes "Memetic Warfare: The Future of War" in the US Army's Military Intelligence Professional Bulletin.
• 2011. DARPA launches SMISC (Social Media in Strategic Communication, solicitation dated 14 July 2011): an estimated $42 million of basic research over three years. Its first goal, verbatim: "detect, classify, measure and track the formation, development and spread of ideas and concepts (memes)". Its programme manager, Rand Waltzman, reckons it ended up costing about $50 million over four years.
• 2016. Jeff Giesea publishes "It's Time to Embrace Memetic Warfare" in the journal of NATO's Strategic Communications Centre of Excellence in Riga: "Trolling, it might be said, is the social media equivalent of guerrilla warfare, and memes are its currency of propaganda". It's an opinion essay, not doctrine. The Centre's website dates it 1 March 2016, though it is often cited as 2015.

The great powers don't write about memes, but they do write about the terrain memes live on:

• Russia says informatsionnoye protivoborstvo, "information confrontation", rather than "war". In its Ministry of Defence's Conceptual Views (2011), information war includes "massive brainwashing of the population for destabilizing the society and the state". General Gerasimov's famous article (26 February 2013) says non-military means have "in many cases exceeded the power of force of weapons". Be careful with the label, though. The "Gerasimov doctrine" was coined by analyst Mark Galeotti on his blog on 6 July 2014, and he retracted it himself in Foreign Policy in 2018: "There's one small problem. It doesn't exist".
• China set out its "Three Warfares" (public-opinion, psychological and legal warfare) in December 2003, in the People's Liberation Army's political work regulations.
• NATO commissioned a study on cognitive warfare in 2020 (François du Cluzel, January 2021) that proposes treating the human as a sixth operational domain. Its most quoted line: "the individual becomes the weapon". NATO itself warns that the study doesn't express its official position, and the formal concept was still awaiting approval in late 2025.
• The United States made information the seventh joint function in 2017 and issued dedicated doctrine (JP 3-04) in September 2022, without publishing it. We found no official US doctrinal publication that uses the phrase memetic warfare: memes only appear in research solicitations such as SMISC.

NATO's Centre of Excellence has counted real memes. Its May 2016 report on the Ukraine conflict analysed Facebook and VKontakte from 31 December 2012 to 1 April 2014 and found 348 memes, almost six times as many on VKontakte. The per-network figures the report gives (52 and 289) add up to 341, not 348: a discrepancy in the original.

How it's orchestrated: three documented models

The publicly documented cases fit three models. We call them the factory, the volume cannon and the swarm. They differ in four things: who decides, what it costs, how much it reaches real people and whether it can be attributed.

The factory: the Internet Research Agency

Special Counsel Mueller's indictment of St Petersburg's Internet Research Agency (IRA), dated 16 February 2018, is the best public document on how a meme factory works on the inside:

• Budget. By September 2016, "Project Lakhta" exceeded 73 million roubles a month (over $1.25 million), including a million roubles in bonuses. Note that this budget covered the whole project, including Russian and other foreign audiences, not just the US.
• Staff. "Hundreds" of employees. By July 2016, more than 80 were assigned to the US-facing "translator project".
• Into the streets. The operation went as far as organising real rallies on US soil. The @TEN_GOP account passed 100,000 followers.

New Knowledge's analysis for the Senate Intelligence Committee (December 2018) put numbers on the format. The analysts reviewed about 67,000 Facebook memes and more than 100,000 Instagram memes by hand. Their verdict: "Memes are the propaganda of the digital age". Much of the material was recycled popular memes, and the most visual network delivered the most:

The most-quoted reach figure, that 126 million Facebook users "may have been served" IRA content, is the company's own (Colin Stretch before the Senate, 31 October 2017). The direct figure was 29 million. Twitter identified 3,841 accounts linked to the IRA. The Senate report (volume 2, October 2019) highlights something else: the most targeted group was African-Americans, and the fake Blacktivist page racked up 11.2 million engagements. After election day, IRA activity rose, by 238% on Instagram.

The factory 2.0: Doppelganger

In September 2022, EU DisinfoLab and Qurium documented Doppelganger: clones of at least 17 European media outlets (Bild, The Guardian, 20 Minutes, Ansa…) on look-alike domains, pushed with Facebook Pages and fake profiles. Two years later, on 4 September 2024, the US Department of Justice seized 32 domains. It attributed the operation to the firms Social Design Agency and Structura, working under Russia's Presidential Administration.

The FBI affidavit is explicit about the role of memes. Its "Good Old U.S.A. Project" planned "mass distribution of text comments and memes in Facebook and X discussion threads", with messages for six swing states. One planning document set a target of 60,000 comments a month for France and Germany, and the agency kept a list of more than 2,800 influencers in 81 countries. The factory no longer manufactures only content. It manufactures conversation around the content.

The volume cannon: Spamouflage

The Chinese operation known as Spamouflage (Dragonbridge at Google) is the perfect counter-example. In August 2023, Meta removed 7,704 Facebook accounts, 954 Pages, 15 Groups and 15 Instagram accounts, with a presence on more than 50 platforms. Ben Nimmo, then Meta's head of threat intelligence, called it "the largest, though unsuccessful, and most prolific covert influence operation that we know of".

Note the word unsuccessful. Meta found no "substantial engagement among authentic communities". Google, which chases it on YouTube, gives starker numbers. Of the more than 57,000 channels it disabled in 2023, 80% had zero subscribers. Of the more than 900,000 videos removed, 65% had fewer than 100 views and 30% had none. It's a cannon firing into a desert.

The swarm: NAFO, Saint Javelin and the Ghost of Kyiv

The third model has no headquarters and no budget. NAFO is a decentralised network with a shared look (the "fella" dog), a clear goal (ridiculing Kremlin propaganda) and a funding route: the avatars were rewards for donors. Its founder estimates they raised "more than $1 million", although nobody keeps an official tally. Saint Javelin, a Toronto sticker shop selling a Madonna holding a missile launcher, started on 16 February 2022 aiming to raise $30,000. By September 2025 it said it had passed $2.5 million. Both figures come from the organisers themselves.

The swarm also manufactures myths. The Ghost of Kyiv, a pilot said to have downed dozens of Russian jets in the first days of the invasion, spread across every network. On 30 April 2022, Ukraine's own Air Force called it "a superhero legend created by the Ukrainians", a collective symbol of its pilots. It worked for morale and for fundraising. Technically, it was also friendly disinformation.

The three models in one table

The table's lesson is awkward for anyone who thinks in budgets: volume doesn't buy reach. The factory that worked did so because it exploited real fractures (race, identity) in the right format (images, Instagram). The biggest cannon we know of had almost no audience.

When the platform is the weapon: Myanmar

In one case nobody needed a factory. The UN Fact-Finding Mission on Myanmar (report A/HRC/39/64, September 2018) wrote that Facebook "has been a useful instrument for those seeking to spread hate, in a context where for most users Facebook is the Internet", and that the company's response had been "slow and ineffective". Nearly 725,000 Rohingya fled the country after August 2017. The report Facebook itself commissioned from BSR (November 2018) admitted it: "we weren't doing enough". Sometimes the parallel front isn't opened by an army but by an unmoderated ecosystem.

And the caliphate: ISIS on Twitter

Before anyone else, the Islamic State understood the network as a front. Berger and Morgan's census for Brookings (March 2015) counted at least 46,000 supporter accounts between September and December 2014, with an upper bound of about 70,000. Each had about a thousand followers on average, and a core of 500 to 2,000 hyperactive accounts drove most of the reach. Three in four wrote in Arabic.

Why they spread

Why the IRA's factory delivered and the Chinese cannon didn't comes down to three solid findings in the literature.

Falsehood travels further, and not because of bots. Vosoughi, Roy and Aral analysed in Science (2018) some 126,000 stories spread by about 3 million people on Twitter between 2006 and 2017. Falsehoods were 70% more likely to be retweeted than the truth. It took the truth about six times as long to reach 1,500 people. The key point is that bots accelerated true and false news at the same rate: falsehood spreads further "because humans, not robots, are more likely to spread it". The authors point to novelty, and to the surprise or disgust it provokes.

Moral emotion is fuel. Brady and colleagues (PNAS, 2017) studied 563,312 messages on polarised issues. Each additional moral-emotional word increased a message's diffusion by about 20%, and the effect was stronger within each ideological bubble than between them. A well-made war meme is exactly that: moral outrage compressed into an image.

Memes are born on the fringes and then jump. Zannettou and colleagues (IMC 2018) tracked about 160 million images across Twitter, Reddit, 4chan's /pol/ and Gab over thirteen months in 2016–2017. They used perceptual hashing, a 64-bit fingerprint that survives crops and edits. /pol/ was the biggest meme factory (1.57 million posts), but The_Donald was the most efficient community at pushing memes out to the rest. The seeding happens in small, radical niches; the harvest, on the big platforms.

How impact is measured

If volume isn't impact, you need another yardstick. The one analysts and platforms use most is Ben Nimmo's Breakout Scale (Brookings, September 2020). It doesn't count how many messages are posted. It measures how far a campaign breaks out of its niche:

By this yardstick, the big fear of recent years, generative AI, stays in proportion for now. In May 2024, OpenAI disclosed five operations that used its models: Bad Grammar and Doppelganger (Russia), Spamouflage (China), IUVM (Iran) and Zero Zeno, run by a commercial firm in Israel. None scored above Category 2: "no breakout to authentic audiences". By October 2025 the running total had passed 40 networks disrupted since February 2024. Its conclusion: actors bolt AI onto workflows they already had rather than invent new ones. Microsoft (April 2024) points the same way: AI-enhanced content is more influential than fully AI-generated content, and fake audio more than fake video.

A methodological caveat: these "limited impact" assessments come from the platforms themselves, about what they find. What they don't find never makes the statistics.

How it's detected

Platforms don't take these networks down for what they say, but for how they behave. Meta calls it Coordinated Inauthentic Behavior (CIB): "coordinated efforts to manipulate public debate for a strategic goal, in which fake accounts are central to the operation". It has published its takedowns since 2017. By December 2022 it had removed more than 200 networks from 68 countries, and the ranking of origins is stable: Russia (39 networks by August 2024), Iran (about 30) and China (11).

The reference academic method is Pacheco and colleagues' (ICWSM 2021). You pick a behavioural trace, build an account-to-trace network and project it onto the accounts: two accounts are linked if they share too many traces. Finally, an analyst reviews the groups by hand. Its strength is that it detects coordination, not automation, so it catches human-run operations that a bot detector misses. Those detectors fail a lot, by the way: Rauchfleisch and Kaiser (PLOS ONE, 2020) showed that Botometer labels many humans as bots and vice versa, especially outside English.

For memes, the natural trace is the image's perceptual hash, the same one Zannettou and colleagues used. This is the core of the method in dependency-free Python with synthetic data: 60 organic accounts post random memes over a week, and a network of 6 accounts posts five waves of the same meme within 40 seconds.

"""Detecting coordinated behaviour through co-posting (synthetic data, standard library only).

Idea (Pacheco et al., ICWSM 2021): two accounts that post the same content, again and again,
seconds apart, rarely do so by chance. Count how often each pair coincides within a window,
keep the pairs above a threshold and group them.
"""
import random
from collections import defaultdict

random.seed(7)
WINDOW = 60     # seconds: two posts of the same meme closer than this "coincide"
THRESHOLD = 3   # minimum coincidences to call a pair of accounts coordinated

# --- Synthetic data: (account, perceptual hash of the meme, second it was posted) ---
events = []
memes = [f"meme_{i:02d}" for i in range(30)]
for c in range(60):                                   # 60 organic accounts
    for _ in range(random.randint(3, 12)):
        events.append((f"user_{c:02d}", random.choice(memes), random.randint(0, 7 * 86400)))
network = [f"net_{c}" for c in range(6)]              # 6 accounts in a coordinated network
for wave, meme in enumerate(["meme_03", "meme_11", "meme_17", "meme_22", "meme_29"]):
    t0 = 86400 * (wave + 1)
    for account in network:
        events.append((account, meme, t0 + random.randint(0, 40)))   # all within 40 s

# --- 1. Coincidences per pair: same meme inside the window ---
by_meme = defaultdict(list)
for account, meme, t in events:
    by_meme[meme].append((t, account))
pairs = defaultdict(int)
for meme, posts in by_meme.items():
    posts.sort()
    for i, (t1, a) in enumerate(posts):
        for t2, b in posts[i + 1:]:
            if t2 - t1 > WINDOW:
                break
            if a != b:
                pairs[tuple(sorted((a, b)))] += 1

# --- 2. Pairs above the threshold → groups (union-find) ---
parent = {}
def root(x):
    parent.setdefault(x, x)
    while parent[x] != x:
        parent[x] = parent[parent[x]]
        x = parent[x]
    return x
for (a, b), n in pairs.items():
    if n >= THRESHOLD:
        parent[root(a)] = root(b)
groups = defaultdict(set)
for account in parent:
    groups[root(account)].add(account)

print(f"events: {len(events)} · accounts: {len({e[0] for e in events})} · pairs that ever coincide: {len(pairs)}")
print(f"pairs with >= {THRESHOLD} coincidences within {WINDOW} s: {sum(1 for n in pairs.values() if n >= THRESHOLD)}")
for g in sorted(groups.values(), key=len, reverse=True):
    print(f"coordinated group ({len(g)} accounts): {', '.join(sorted(g))}")

Expected output:

events: 497 · accounts: 66 · pairs that ever coincide: 17
pairs with >= 3 coincidences within 60 s: 15
coordinated group (6 accounts): net_0, net_1, net_2, net_3, net_4, net_5

Of 17 pairs that ever coincide, only 15 do so three times or more, and they are exactly the 15 possible pairs among the network's 6 accounts. Chance coincidences between organic users stay below the threshold. Real data is never this clean: you have to calibrate the window and threshold, down-weight very popular memes (which coincide through sheer virality) and review every group by hand before calling it a network. But this is the logic behind platform takedowns: the content can be legitimate; the choreography can't.

How it's managed and shut down

Detecting isn't responding. On the available evidence, the response takes the shape of a loop, and the first decision is the most counter-intuitive:

Before: inoculate

Psychological inoculation, or prebunking, teaches the manipulation technique before the specific message arrives. Roozenbeek, van der Linden and colleagues (Science Advances, 2022) tested it in the lab and on YouTube. In the field, 5.4 million US users saw the ads and 22,632 answered a test question. Recognition of techniques such as scapegoating and false dichotomies improved by about 5%. That's little per person but a lot at scale, since it costs the price of an ad.

Google Jigsaw took the idea to Poland, Czechia and Slovakia in autumn 2022, against hoaxes about Ukrainian refugees. Its videos passed 38 million views, but the effect was uneven: largest in Poland, mixed in Czechia and close to zero in Slovakia, where the videos were dubbed rather than produced locally. The vaccine works best with a local accent.

During: answer fast, and with wit

Taiwan turned this into state policy under the motto "humor over rumor". In February 2020 the Premier answered a hoax that was causing panic-buying of toilet paper with a meme. The operating rule attributed to the government, the "2-2-2", exists in several public versions: 20 minutes or 2 hours to respond, 200 words and 2 images. We found no official document fixing the exact version. What stays constant is the idea: speed, brevity and an image, the same weapons as the meme being answered.

Debunking doesn't backfire. The feared "backfire effect" (a correction that strengthens the false belief) is rare. Wood and Porter (Political Behavior, 2019) ran 5 experiments with more than 10,100 people on 52 issues: no correction produced a backfire.

Context notes work, but they arrive late. X's Community Notes are the largest natural experiment. On the specific post the effect is strong; across the platform it's modest, because the note appears after the viral moment has passed:

After: take down, and watch where they go

Network takedowns (CIB) are the platforms' bluntest tool and the one that leaves the clearest public record. In 2018 Twitter opened an archive of state-backed operations with the IRA's 3,841 accounts and 770 from Iran. By 2019 it held more than 30 million tweets.

Removing people (deplatforming) has stronger evidence behind it than is often assumed, with one caveat:

• McCabe and colleagues (Nature, 2024) studied the removal of 70,000 misinformation-spreading accounts after 6 January 2021, using a panel of more than 500,000 users. Misinformation circulation fell, both from the banned accounts and from their followers. The authors can't isolate the exact size of the effect, because it coincided with the storming of the Capitol.
• Jhaver and colleagues (2021) measured, across 49 million tweets, the removal of Alex Jones, Milo Yiannopoulos and Owen Benjamin: conversation about them dropped sharply, and so did their supporters' toxicity.
• The caveat: those who migrate to a platform of their own lose audience but can radicalise. Horta Ribeiro and colleagues (2021) saw it with r/The_Donald.

Above it all: rules and the state

• The European Union. Since 25 August 2023, the Digital Services Act has required the largest platforms to assess and mitigate systemic risks to civic discourse. Its first formal proceedings were opened against X on 18 December 2023, after the wave of disinformation about the Israel–Hamas war; the maximum fine is 6% of global turnover. The Code of Practice on Disinformation became a code of conduct under the Act on 1 July 2025.
• Measured attacks. The EUvsDisinfo database holds more than 18,000 cases. The European External Action Service's reports on foreign manipulation counted 100 incidents in 2022 (Russia in 88), 750 in 2023 and 505 in 2024.
• Sanctions. The EU suspended RT and Sputnik on 1 March 2022. The US paired the Doppelganger domain seizures with sanctions on 10 individuals and 2 entities the same day.
• And defences get switched off too. The State Department's Global Engagement Center, the US office for countering foreign propaganda, closed on 23 December 2024. Its successor, R/FIMI, was shut on 16 April 2025. Shutting down a memetic war also depends on someone still being willing to fight it.

And counter-messaging in uniform

The British Army has a unit for this, the 77th Brigade, which describes itself as a hybrid unit to "combat new forms of warfare in the information environment". Freedom of Information requests revealed that it monitored disinformation trends on UK social media during COVID. It's a reminder that defensive tools can also point inwards.

Method note: what this is and what it isn't

• It is a synthesis of primary sources: court documents, platform reports, parliamentary and UN reports, and peer-reviewed papers. Every figure comes with its source.
• It isn't our own measurement of impact. Most reach figures come from the platforms themselves (Meta, Google, Twitter/X, OpenAI), and the independent analyses, such as New Knowledge's, worked on data those same platforms supplied. What they don't detect doesn't show up.
• It isn't a manual. We describe how the documented campaigns were organised and how they are detected. We give no instructions for creating fake profiles or evading detection.
• Some figures are self-reported and we couldn't audit them (what NAFO and Saint Javelin raised), and some rules have no official document (Taiwan's "2-2-2"). We say so where they appear.
• The code is a teaching example with synthetic data, not a production-ready detector.

What we take away

• Volume doesn't buy reach. The biggest operation we know of, Spamouflage, barely had an audience. The one that worked, the IRA, exploited real fractures in the right format. Measure breakout (Categories 1–6), not messages.
• People push memes, not bots. Falsehood travels 70% further on novelty and moral emotion, and bots don't change that. Defence has to speak to people.
• You detect the choreography, not the opinion. Takedowns that hold up are based on coordinated behaviour, not on content. That's what makes them defensible in a democracy.
• Speed decides the shutdown. Inoculate early (prebunking), answer fast and with wit, annotate early and take the network down. A note that arrives late cuts 46% of what's left, but only 12% of the total.

Sources and links

Doctrine and history

• Dawkins, R. (1976). The Selfish Gene, ch. 11, "Memes: the new replicators". Oxford University Press.
• Hancock, B. J. (2010). "Memetic Warfare: The Future of War". Military Intelligence Professional Bulletin 36(2). irp.fas.org
• DARPA (2011). Social Media in Strategic Communication (DARPA-BAA-11-64). darpa.mil
• Giesea, J. (2016). "It's Time to Embrace Memetic Warfare". Defence Strategic Communications 1. NATO StratCom COE. stratcomcoe.org
• Galeotti, M. (2018). "I'm Sorry for Creating the 'Gerasimov Doctrine'". Foreign Policy. foreignpolicy.com
• Russian Ministry of Defence (2011). Conceptual Views Regarding the Activities of the Armed Forces of the Russian Federation in the Information Space. nsarchive.gwu.edu
• du Cluzel, F. (2021). Cognitive Warfare. NATO ACT Innovation Hub. innovationhub-act.org
• NATO StratCom COE (2016). Framing of the Ukraine–Russia Conflict in Online and Social Media. stratcomcoe.org

Cases

• United States v. Internet Research Agency et al. (2018), indictment. justice.gov
• New Knowledge (2018). The Tactics & Tropes of the Internet Research Agency. law.shu.edu
• EU DisinfoLab and Qurium (2022). Doppelganger. nsarchive.gwu.edu
• US Department of Justice (2024). Doppelganger affidavit. justice.gov
• Google TAG (2024). Dragonbridge. blog.google
• Independent International Fact-Finding Mission on Myanmar (2018). A/HRC/39/64. ecoi.net
• Berger, J. M. and Morgan, J. (2015). The ISIS Twitter Census. Brookings. brookings.edu
• Kyiv Post (2023). Interview with NAFO's founder. kyivpost.com

Mechanics, measurement and detection

• Vosoughi, S., Roy, D. and Aral, S. (2018). "The spread of true and false news online". Science 359(6380). doi:10.1126/science.aap9559
• Brady, W. J. et al. (2017). "Emotion shapes the diffusion of moralized content in social networks". PNAS 114(28). doi:10.1073/pnas.1618923114
• Zannettou, S. et al. (2018). "On the Origins of Memes by Means of Fringe Web Communities". IMC. arxiv.org
• Nimmo, B. (2020). "The Breakout Scale". Brookings. brookings.edu
• Pacheco, D. et al. (2021). "Uncovering Coordinated Networks on Social Media". ICWSM. ojs.aaai.org
• Rauchfleisch, A. and Kaiser, J. (2020). "The False Positive Problem of Automatic Bot Detection in Social Science Research". PLOS ONE. doi:10.1371/journal.pone.0241045
• OpenAI (2024). AI and Covert Influence Operations: Latest Trends. openai.com
• DISARM Foundation. An open framework for describing influence operations. disarm.foundation

Response

• Roozenbeek, J. et al. (2022). "Psychological inoculation improves resilience against misinformation on social media". Science Advances. cam.ac.uk
• Wood, T. and Porter, E. (2019). "The Elusive Backfire Effect". Political Behavior 41. doi:10.1007/s11109-018-9443-y
• Slaughter, I. et al. (2025). The effect of Community Notes. PNAS. pmc.ncbi.nlm.nih.gov
• McCabe, S. D. et al. (2024). "Post-January 6th deplatforming reduced the reach of misinformation on Twitter". Nature 630. doi:10.1038/s41586-024-07524-8
• Jhaver, S. et al. (2021). "Evaluating the Effectiveness of Deplatforming as a Moderation Strategy on Twitter". Proc. ACM HCI. doi:10.1145/3479525
• European Commission. Code of Practice on Disinformation. digital-strategy.ec.europa.eu
• EEAS (2024). 2nd EEAS Report on Foreign Information Manipulation and Interference Threats. eeas.europa.eu