Presentation groups

Brief history (cf. Conway, Scrivens, and Macnair 2019)

far right use of online tools (Zhang and Davis 2022)

• distribute information
  • most extreme cases: spreading ‘attacker manifestos’
    • a focal point for platforms and states in recent years
• produce subculture (content containing frames, narratives, and that generates collective identity)
• fundraise
• lobby policy-makers
• transnational organisation and mobilisation
  • examples: ‘#FreeTommy’, anti-Istanbul Convention, EU elections
  • other examples? what do those examples tell us?

see also Wahlström and Törnberg (2019)

Cryptic codes

• Far-right actors online often use ‘wolf-in-sheep’s-clothes strategy’ (Zhang and Davis 2022): camouflage extremist ideas with in-jokes and coded language

• Other examples? (‘Let’s go Brandon!’, the ‘okay’ hand symbol)

Spotlight on far-right financing online

Spotlight on far-right financing online

Spotlight on far-right financing online

Spotlight on far-right financing online

Spotlight on far-right financing online

1. Donations/self-funding (e.g., Blazakis 2022; Bogle 2021)

• use of mainstream platforms (PayPal, Patreon, Kickstarter, Monero, and GoFundMe)
  • but counter-mobilisation has pressured these platforms (Kienzl 2019; Rietschel 2019)
    • part of widespread strategy: ‘bankrupt’ the far right (Ortiz 2022) (e.g., against 2017 Charlottesville, U.S. rally)
  • ‘hacking’ of some platforms (GiveSendGo) (Wilson 2021)
• alternative funding platforms used (Hatreon, Goyfundme): less reach, but less risk of lock-out
• crypto-currencies: difficult to trace, large transfers (Sick and Jost 2023; Hussain 2021)—but still outside mainstream

2a. Sale of goods (merchandise, music, real estate, etc)

• far-right rap (Raabe 2023) and rock (Dych 2016)—big business
• fascist fashion (Postma 2022)
  • explicitly far-right brands: e.g., Thor Steinar (here, counter-mobilisation too)
  • co-opted brands: e.g., Lonsdale
• real estate: create ‘safe hinterlands’ and/or boost a group’s resources (Spiegel doku, NDR doku, AA Stiftung report)
  • NPD offered pamphlets on including the party in one’s will…
• key question: on which platforms do these transactions happen
  • mainstream markets being ‘misused’ (Al-Othman 2024)
  • websites of far-right groups or companies

2b. Sale of services (memberships, events, etc)

Far-right music events (Germany, Verfassungschutzberichte)

Concert = [
  {Year: "2009", Num: 125}, 
  {Year: "2010", Num: 128}, 
  {Year: "2011", Num: 131}, 
  {Year: "2012", Num: 82}, 
  {Year: "2013", Num: 78}, 
  {Year: "2014", Num: 55}, 
  {Year: "2015", Num: 63}, 
  {Year: "2016", Num: 68}, 
  {Year: "2017", Num: 68}, 
  {Year: "2018", Num: 60}, 
  {Year: "2019", Num: 64},
  {Year: "2020", Num: 27}, 
  {Year: "2021", Num: 18}, 
  {Year: "2022", Num: 35}, 
  {Year: "2023", Num: 39}, 
  {Year: "2024", Num: 29},
]

SongEvening = [
  {Year: "2009", Num: 38}, 
  {Year: "2010", Num: 40}, 
  {Year: "2011", Num: 30}, 
  {Year: "2012", Num: 17}, 
  {Year: "2013", Num: 47}, 
  {Year: "2014", Num: 55}, 
  {Year: "2015", Num: 54}, 
  {Year: "2016", Num: 71}, 
  {Year: "2017", Num: 88}, 
  {Year: "2018", Num: 95}, 
  {Year: "2019", Num: 133},
  {Year: "2020", Num: 55}, 
  {Year: "2021", Num: 59}, 
  {Year: "2022", Num: 92}, 
  {Year: "2023", Num: 132}, 
  {Year: "2024", Num: 130},
]

Other = [
  {Year: "2009", Num: 42}, 
  {Year: "2010", Num: 71}, 
  {Year: "2011", Num: 57}, 
  {Year: "2012", Num: 49}, 
  {Year: "2013", Num: 46}, 
  {Year: "2014", Num: 51}, 
  {Year: "2015", Num: 82}, 
  {Year: "2016", Num: 84}, 
  {Year: "2017", Num: 103}, 
  {Year: "2018", Num: 115}, 
  {Year: "2019", Num: 114},
  {Year: "2020", Num: 59}, 
  {Year: "2021", Num: 67}, 
  {Year: "2022", Num: 130}, 
  {Year: "2023", Num: 151}, 
  {Year: "2024", Num: 148},
]

// Create a dropdown which dataset to display (Inputs.select) - Inputs.radio for radio buttons
// FIRST VERSION: viewof dataset = Inputs.radio(["Concert", "SongEvening", "Other"], {label: "Dataset"})
viewof datasets2 = Inputs.checkbox(
  ["Concert", "SongEvening", "Other"],
  {label: "", value: ["Concert", "SongEvening", "Other"]}
)

// Choose the corresponding dataset based on the selection
// FIRST VERSION: selected_data = dataset === "Concert" ? Concert : dataset === "SongEvening" ? SongEvening : Other
// Combine the selected datasets into one array
selected_data2 = datasets2.flatMap(dataset => {
  if (dataset === "Concert") {
    return Concert.map(d => ({...d, dataset: "Concert"}));
  } else if (dataset === "SongEvening") {
    return SongEvening.map(d => ({...d, dataset: "SongEvening"}));
  } else if (dataset === "Other") {
    return Other.map(d => ({...d, dataset: "Other"}));
  }
  return [];
});

// plot as a bar plot with dodged bars coloured by dataset
Plot.plot({
  x: {
    label: "",
    tickSize: 2,
    tickRotate: -90,
    tickFormat: d => d.toString()
  },
  y: {
    label: "",
    grid: true
  },
  color: {
    legend: "ramp",
    domain: ["Concert", "SongEvening", "Other"],
    range: ["#800000", "#008080", "#FFD700"], // https://en.wikipedia.org/wiki/Web_colors
    swatchSize: 60, // https://observablehq.com/plot/features/legends
    marginLeft: 10,
    width: 600,
    height: 80,
    className: "large-font"
  },
  margin: 100,
  marks: [
    Plot.barY(selected_data2, {
      x: "Year",
      y: "Num",
      fill: "dataset",
      groupby: "dataset",
      x1: "dataset"
    })
  ],
  width: 1200,
  height: 500,
  style: {
    fontSize: 40
  }
})

3. Criminal activities

• production and/or sale of drugs (Ibsen et al. 2020)
• cyber-crime
  • hacking computer networks
  • sale or distribution of violence-enabling tools
  • more accessible (for now?) through manipulation of generative AI tools (e.g., Telegraph 2024)

Networking transnationally (Heft et al. 2021)

To what extent do [alternative right-wing online news sites (RNS)] in the United States, United Kingdom, Germany, Austria, Sweden, and Denmark form national and transnational right-wing news ecologies through hyperlinking?

Networking transnationally (Heft et al. 2021)

Multi-platform strategies (Mitts 2025)

militant and hate organizations’ online success centers on their ability to operate across many platforms in parallel

• content moderation “organized practice of screening user-generated content (UGC) posted to Internet sites, social media, and other online outlets, in order to determine the appropriateness of the content for a given site, locality, or jurisdiction.”

Multi-platform activity (Mitts 2025) - regulation challenges

• assumption: stronger action by platform companies will decrease their ability to exploit the internet
  • this assumption is plausible in isolated platform perspective—less so in multi-platform perspective
  • platforms largely moderate content in isolation, but extremist actors coordinate activity across multiple platforms
• adaptation mechanisms:
  • platform migration: move to alternative platforms
  • messaging: moderate discourse on regulated platforms
  • mobilisation: problematise platforms’ content moderation policies/practices

Regulation approaches (Gorwa 2024)

• contest
• collaborate
• convince

Regulation approaches (Gorwa 2024)

• collaborate
• convince

• legally binding, enforceable rules
  • executive orders; legislatures pass laws (e.g., data protection, competition regulation, consumer safety; cybersecurity)

Regulation approaches (Gorwa 2024)

• contest
• collaborate
• convince

Regulation approaches (Gorwa 2024)

• contest
• convince

• non-binding, voluntarily enacted rules designed with government input, occasionally featuring binding procedural constraints
  • may be agreed by a mix of industry, firm, and civil society stakeholders → implemented voluntarily by industry

Regulation approaches (Gorwa 2024)

• contest
• collaborate
• convince

Regulation approaches (Gorwa 2024)

• contest
• collaborate

• using existing channels to raise grievances rather than striving for new rules
  • often occurs in the shadows, few public-facing elements
  • ‘cheap’ to pursue and implement → but also ‘cheap’ consequences (no binding sanctions, probably least impactful)

Regulation approaches (Gorwa 2024)

• contest - legally binding, enforceable rules
• collaborate - non-binding, voluntarily enacted rules designed with government input, occasionally featuring binding procedural constraints
• convince - using existing channels to raise grievances rather than striving for new rules

Major extant regulation, forums, etc. (cf. Gorwa 2024; Conway et al. 2023)

• Germany’s ‘Network Enforcement Law’ (NetzDG)
• France’s Avia Law (declared unconstitutional and revised drastically)
• EU’s Digital Services Act (DSA)
• Terrorist Content Online (TCO) Regulation
• Christchurch Call
• European Union Internet Forum (EUIF)
• Global Internet Forum for Countering Terrorism (GIFCT)
  • hash-sharing database
• Tech Against Terrorism

‘dangerous actors’ on Meta (Biddle 2021)

Extreme right, Islamist, Drug cartel, Extreme left, Buddhist nationalist, Separatist

CasaPound v. Facebook (e.g., Golia and Behring 2020)

• CasaPound (e.g., Froio et al. 2020): (neo-)‘fascist’ organisation in Italy, highly active on digital media

• 9.9.2019: Facebook deactivates CasaPound page (and representatives), arguing the content is ‘hate speech’ and ‘incitement to violence’, violating Facebook’s Terms of Use

• CP argues (before Court of Rome) ‘it proposed an update of historical fascism that exclusively values its social policies, and that it has publicly condemned racial laws’; that it does not violate Facebook’s terms of service — and that CP protected by article 21 of the Italian constitution

• see further at: https://globalfreedomofexpression.columbia.edu/cases/casapound-v-facebook/

md`## Poll results (Respondents: ${respondentCount}) and CasaPound v. Facebook`

• what is at stake here? who decides? who ought to?

import { liveGoogleSheet } from "@jimjamslam/live-google-sheet";
import { aq, op } from "@uwdata/arquero";

// UPDATE THE LINK FOR A NEW POLL
surveyResults = liveGoogleSheet(
  "https://docs.google.com/spreadsheets/d/e/" +
    "2PACX-1vRk0WyoY9IFIZd6XjeguLMKR-vv1RKAcSOGoiWr5lMgw1oSQo6TiJH6DkEO3DcyNSYJoiqvDIEV58fY/" +
    "pub?gid=418161089&single=true&output=csv",
  10000, 1, 6); // adjust the last number to select all relevant columns 

respondentCount = surveyResults.length;

defineCounts = aq.from(surveyResults)
  .select("define")
  .groupby("define")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
define_maxCountRE = Math.max(...defineCounts.objects().map(d => d.count));

plot_define = Plot.plot({
  marks: [
    Plot.barY(defineCounts, {
      x: "define",
      y: "count",
      fill: "define",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "state actors",
      "platform companies",
      "independent regulator",
      "other"
    ],
    range: [
      "teal",
      "indigo",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 200,
  x: { label: "", tickSize: 2, tickRotate: -30, padding: 0.2,
    domain: ["state actors", "platform companies", "independent regulator", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(defineCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, define_maxCountRE]
  },
  facet: { data: defineCounts, x: "measure", label: "" },
  marginLeft: 80,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

policyCounts = aq.from(surveyResults)
  .select("policy")
  .groupby("policy")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
policy_maxCountRE = Math.max(...policyCounts.objects().map(d => d.count));

plot_policy = Plot.plot({
  marks: [
    Plot.barY(policyCounts, {
      x: "policy",
      y: "count",
      fill: "policy",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "state actors",
      "platform companies",
      "independent regulator",
      "other"
    ],
    range: [
      "teal",
      "indigo",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 200,
  x: { label: "", tickSize: 2, tickRotate: -30, padding: 0.2,
    domain: ["state actors", "platform companies", "independent regulator", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(policyCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, policy_maxCountRE]
  },
  facet: { data: policyCounts, x: "measure", label: "" },
  marginLeft: 80,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

CasaPound v. Facebook (e.g., Golia and Behring 2020)

• Court decision:
  • ‘Facebook holds a special position and its mission aims to uphold freedom of expression’
  • CP page deactivation violated its rights as a political party (under article 49 of the Constitution)
  • ordered FB to reactivate page(s) and pay a penalty of 800 EUR for each day of deactivation

CasaPound v. Facebook (e.g., Golia and Behring 2020)

• Facebook appealed (unsuccessfully), saying it is ‘a private company operating for profit protected by art. 41 of the Constitution’, that:

the order had erroneously attributed a special nature to the contract between the social network and the user, when it was instead an ordinary contract under civil law. In the absence of any legal basis, according to Facebook, it is not possible to attribute public service obligations to private sector players such as the protection of freedom of association and expression. Likewise, Facebook argued that it is not required to ensure special protection to some users such as organizations engaged in political activities by virtue of their role in the political debate.

Predominant approach and deplatforming

approachCounts = aq.from(surveyResults)
  .select("approach")
  .groupby("approach")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
approach_maxCountRE = Math.max(...approachCounts.objects().map(d => d.count));

plot_approach = Plot.plot({
  marks: [
    Plot.barY(approachCounts, {
      x: "approach",
      y: "count",
      fill: "approach",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "do nothing",
      "attach warning",
      "reduce possible views",
      "permanently remove post",
      "suspend poster's account"
    ],
    range: [
      "teal",
      "goldenrod",
      "darkorange",
      "darkolivegreen",
      "darkred"
    ]
  },
  marginBottom: 260,
  x: { label: "", tickSize: 2, tickRotate: -30, padding: 0.2,
    domain: ["do nothing", "attach warning", "reduce possible views", "permanently remove post", "suspend poster's account"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(approachCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, approach_maxCountRE]
  },
  facet: { data: approachCounts, x: "measure", label: "" },
  marginLeft: 80,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

deplatformingCounts = aq.from(surveyResults)
  .select("deplatforming")
  .groupby("deplatforming")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
deplatforming_maxCountRE = Math.max(...deplatformingCounts.objects().map(d => d.count));

plot_deplatforming = Plot.plot({
  marks: [
    Plot.barY(deplatformingCounts, {
      x: "deplatforming",
      y: "count",
      fill: "deplatforming",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Yes",
      "No",
      "Maybe"
    ],
    range: [
      "forestgreen",
      "darkred",
      "goldenrod"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -1, padding: 0.2,
    domain: ["Yes", "No", "Maybe"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(deplatformingCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, deplatforming_maxCountRE]
  },
  facet: { data: deplatformingCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

Deplatforming effects

• diminishing the scale of influence (Ghaffary 2022)
  • Facebook, Youtube: billions of users
  • Parler, Gettr (e.g.): at most a few million users
  • Telegram: a few hundred million users, little regulation
    • e.g., Proud Boys’ use (Bailard et al. 2024)
    • more emphasis on content moderation after CEO’s arrest in 2024 …

Deplatforming effects (Thomas and Wahedi 2023)

• RQ: How does removing the leadership of online hate organizations from online platforms change behavior in their target audience?
• cases: six network disruptions (i.e., deplatforming) on Facebook
  • NB: the researchers are/were Meta employees
• finding: network disruptions reduced the consumption and production of hateful content

The results suggest that strategies of targeted removals, such as leadership removal and network degradation efforts, can reduce the ability of hate organizations to successfully operate online.

• BUT, finding comes from looking at one platform in isolation

Deplatforming effects (Chandrasekharan et al. 2017)

• 10 June 2015, Reddit banned several subreddits, including: r/fatpeoplehate and r/CoonTown
• RQ1: What effect did Reddit’s ban have on the contributors to banned subreddits?
• RQ2: What effect did the ban have on subreddits that saw an influx of banned subreddit users?
• findings:
  • many users from banned subreddits became inactive
    • led to a drop in Reddit users (some migrated to other platforms)… what’s the significance of this finding?
  • volume of active users’ posting mostly unchanged
  • a dramatic decrease in hate speech usage by the treatment users post-ban

Content moderation, deplatforming legitimacy (Pradel et al. 2024)

• key concept: Toxic speech as consisting of…
  1. incivility,
  2. intolerance, and
  3. violent threats
• experimental design: randomly exposed people (in U.S.) to toxic speech social media posts → effect on users’ content moderation preferences

Moderation, deplatforming legitimacy (Pradel et al. 2024)

Moderation, deplatforming legitimacy (Pradel et al. 2024)