Simeone, M., & Corman, S. R. (2025). Network ripple effects: How Twitter deplatforming flipped authority structure and discourse of the Arizona Election Review community. Sage Open, 15, 21582440251314538. https://doi.org/10.1177/21582440251314538
Summary
This paper examines what happened inside the Arizona Election Review (AER) Twitter community after Twitter’s July 27, 2021 suspension of eight accounts promoting state-level “audits” of the 2020 election. Combining network analysis with corpus linguistics on ~381,000 tweets bracketing the suspensions, Simeone and Corman argue that deplatforming produced ripple effects vastly exceeding the mechanical loss of banned accounts and their followers: the right-leaning cluster’s hub/authority structure collapsed, participation dropped sharply, and the surviving discourse pivoted from active ballot-surveillance recruiting to abstract defiance and meta-conspiracies about deplatforming itself. The authors frame this as evidence that targeted suspensions function as effective “network interventions,” and they question whether the visibility-reduction strategies favored under post-Musk X can replicate this disruption.
Key Contributions
- An in-situ case study of deplatforming effects on the community left behind, rather than the more common study of platform migration to alt-tech sites.
- A comparative analytic frame using before, after, and a counterfactual “before-minus-banned-accounts” network to separate direct removal from emergent ripple effects.
- Empirical demonstration that suspending a small number of central accounts can dismantle hub-and-authority structures sustaining election-denial mobilization.
- Connects deplatforming research to the “insurrectionist playbook” literature, situating moderation as an interruption point in election-conspiracy amplification.
- Raises a policy concern that visibility-reduction may underperform suspension when targets are already popular mobilizers.
Methods
Tweets matching (arizona AND audit) OR #ArizonaAudit OR #fraudit were collected via Netlytic for June 27–August 27, 2021 (264,412 before; 117,014 after). Directed, weighted edgelists were built from retweets, replies, quotes, and mentions, with conductance cutting (granularity 0.025) to identify left/right clusters. The authors compared degree distributions and Kleinberg hub/authority scores across the before, after, and counterfactual networks. An adapted CooRNet pipeline (10-second retweet threshold) tested for coordinated inauthentic behavior. Corpus linguistic analysis used log-likelihood keyness, KWIC, and collocation comparisons across the two corpora.
Findings
- The after network had ~46% fewer nodes and ~55% fewer links than the before network — far beyond what direct removal predicts.
- The right cluster collapsed from 6,380 to 1,395 nodes (7,676 → 1,437 links); the left cluster contracted only modestly (2,623 → 2,223 nodes).
- Hub concentration in the right cluster’s top 20 nodes collapsed, and authority concentration patterns reversed between camps — effectively flipping the network’s authority configuration.
- Only 10 accounts met the coordinated-retweeting threshold, indicating bot/CIB activity was not driving the network; disengagement was likely from real users.
- Pre-ban keywords (“ballots,” “forensic,” “retweet”) indexed surveillance and mobilization; post-ban keywords (“suspended,” “comply,” “distracted,” “Gab”) indexed defiance and platform migration.
- Surviving discourse depended more on a few high-profile verified accounts (Papadopoulos, Wendy Rogers, Arizona GOP) than on distributed grassroots recruiting.
Connections
This sits closely alongside other deplatforming and moderation-effects work in the register, particularly Copland2025-em and Donovan2025-ws on the politics and consequences of platform interventions, and Bak-Coleman2025-pm on post-moderation user behavior. It also speaks to the election-denial and insurrectionist-amplification literature represented by Starbird2025-jj and Moran2025-qn, and to broader work on coordinated and hyperpartisan mobilization such as Bastos2025-ya and Bastos2025-ol. Methodologically, its network-intervention framing complements platform-governance studies like Rieder2025-ju and Freelon2024-sc.