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REVIEW 2 major objections 6 minor 138 references

Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation

T0 review · 2 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read LLMs act as strategy coaches for deceivers and defenders in a Werewolf-style forum game.

desk verdict A small Werewolf study with a real finding—participants use LLMs as strategy coaches, not just content tools—though the coding needs more rigor before the percentages carry weight. read the letter →

arxiv 2506.07211 v1 pith:ZFX7LU4N submitted 2025-06-08 cs.HC cs.AI

Gionnieve Lim , Bryan Chen Zhengyu Tan , Kellie Yu Hui Sim , Weiyan Shi , Ming Hui Chew , Ming Shan Hee , Roy Ka-Wei Lee , Simon T. Perrault
show 1 more author
Kenny Tsu Wei Choo
This is my paper
classification cs.HCcs.AI
keywords DisinformationInfluenceManipulationStrategiesChatbotUsesLargeLanguageModelCommunicationGameWerewolfLLM-Assisted
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that large language models are used in disinformation settings not only to generate false content or verify claims, but also as strategic advisors. In a controlled Werewolf-inspired forum game with 25 participants, Disinformers, Moderators, and Users all turned to a built-in chatbot for advice on how to play their roles: how to deceive without detection, how to shed suspicion, and how to identify the liar. The authors read this as evidence that LLM assistance to disinformation is broader than content production and detection, extending into planning and coordination for both malicious and virtuous actors. They also find that the chatbot's influence depends on group dynamics, with critical engagement exposing LLM-generated disinformation and distraction or rapport-building letting it pass.

What carries the argument

The central mechanism is a Werewolf-inspired communication game played on a custom online forum platform. Five players per group—one Disinformer, one Moderator, three Users—discussed locally relevant topics with fixed agendas, while all had continuous access to an uncensored open-source chatbot. The game's asymmetric-information structure and deception goals make hidden intents observable; forum posts, votes, reports, chatbot prompts and responses, think-aloud commentary, and post-game interviews were then analysed with reflexive thematic analysis, using an influence guide adapted from established disinformation and persuasion techniques to support participants. This setup lets the authors directly observe the strategies users employ with LLM assistance in a controlled but dynamic setting.

What would settle it

If a similar game (or an analysis of real-world influence-operation logs from platform takedowns) showed no strategy-seeking chatbot prompts—no requests for advice on deceiving, deflecting suspicion, or coordinating—the strategic-advisory claim would be exposed as an artifact of the role-play and checklist design.

Watch

Extended reading notes

Core claim

The study's central discovery is that LLMs act as sword and shield simultaneously: they serve as strategic advisors for every role in a disinformation ecosystem, not merely as informational tools. Disinformers most frequently used the chatbot as a strategist (27% of their interactions), asking how to push an agenda while staying hidden, how to frame false claims so they survive fact-checking, and how to shift blame; Moderators and Users used it to verify claims, identify suspicious content, and seek guidance on drawing out the Disinformer or clearing their own name. The paper's contribution is the mapping of these use cases across roles, showing that LLM-based assistance includes coaching on deception, concealment, and detection, and that its effectiveness is mediated by social dynamics such as group scrutiny, emotional tone, and rapport-building.

Load-bearing premise

The study assumes that a role-playing game with non-professional participants, assigned checklists, and a chatbot is a trustworthy stand-in for how real-world disinformation actors and defenders actually behave.

Editorial extensions

If this is right

  • Disinformation detection should monitor not only AI-generated content but also strategy-seeking interactions, since both malicious and defensive actors use LLMs for planning.
  • Groups that critically engage with chatbot output are more likely to catch LLM-generated disinformation, suggesting that platform designs promoting scrutiny can blunt the sword.
  • The effectiveness gap between uncensored open-source models and guardrailed commercial models may create a differential where defenders have access to better tools than typical malicious actors.
  • Platform design should foster appropriate reliance on LLMs, balancing verification tools with transparency and preserving space for subjective opinion.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If strategic-advisory use generalizes beyond the lab, safety evaluations of LLMs should test not only refusal to generate disinformation but also refusal to coach deception plans under role-play.
  • The game's small, intimate setting maps more naturally to local online communities than to mass-scale influence campaigns; the strategic-advisory role may matter most where trust and rapport are built through sustained interaction.
  • A testable extension would compare Disinformers who sought strategic advice against those who only generated content, to isolate whether coaching actually improves concealment or whether it is the Disinformer's editing and social skill that matters.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. This paper reports a qualitative empirical study (n=25, five five-player groups) in which participants played a custom Werewolf-inspired online forum game with a built-in, uncensored LLM chatbot. Disinformers, Moderators, and Users each pursued role-specific goals, and the authors collected forum interactions, chatbot prompts and responses, think-aloud protocols, surveys, and interviews. Using reflexive thematic analysis, they identify role-specific chatbot use cases and argue that LLMs serve not only as content generators and verifiers but also as strategic advisors that help both malicious and defensive actors plan role-appropriate actions. The paper further describes how group dynamics moderate LLM influence, reports obstacles to effective LLM use, and draws implications for platform design and policy.

Significance. If the central claim holds, the paper broadens the current understanding of LLM-assisted disinformation beyond content generation and detection to include strategic coaching of actors on both sides. The study is valuable for its direct observational design: real-world disinformers are difficult to study, and the game environment allows the authors to capture intentions and adaptive strategies, triangulated from forum logs, chatbot transcripts, think-alouds, interviews, and surveys. The case summaries and participant quotes give the paper concrete texture. The authors are also appropriately candid about the game's external validity in Section 6. However, the paper's headline finding about the chatbot as a 'strategic advisor' rests on a thematic category whose coding is not demonstrated to be reproducible, and the effectiveness claims are not systematically tied to game outcomes. These are load-bearing gaps for the stated contributions.

major comments (2)
  1. [§4.1, Table 4, Figure 5, §5.1.1] The paper's central novelty—that LLMs serve as strategic advisors, not merely content generators or verifiers—rests on the code 'Chatbot as strategist' (n=27, 27% of Disinformer interactions). The Table 4 definition ('Use chatbot for ideas on how to play the role') is broad enough to include asking for phrasing, counterarguments, or role-relevant content, and no codebook, category-boundary rules, or inter-rater reliability check (or a documented audit trail) are reported for the reflexive thematic analysis. Because the paper presents these counts as quantitative support for the claim, coding consistency is not merely a methodological nicety. Moreover, with only five Disinformers, one participant (G5D) may account for a large share of the 27 'strategist' interactions; G5D is repeatedly quoted as treating the chatbot as a strategist, and Figure 5 does not provide per-participant counts. The manuscript therefore does not rule out the possibility that the aggregate 27% is driven by a single participant's extended role-playing prompts. Please report per-participant counts for the categories in Table 4, provide a more operationalized coding scheme with boundary examples, and either add an inter-coder reliability check or justify why the quantitative percentages can stand without one; if the reliability evidence cannot be supplied, the 'strategic advisor' claim should be reframed as a hypothesis rather than a demonstrated finding.
  2. [§4.5, §5.3] The paper claims to uncover 'varying efficacy' of LLMs depending on role and strategy, and §5.3 asserts that successful Disinformers exercised greater control over chatbot outputs and 'performed better, receiving fewer suspicion votes (G4D, G3D, G5D)'. However, no analysis systematically links chatbot-use categories to objective game outcomes such as detection, suspicion votes, or stance shifts. The group summaries in Tables 3 and 5 show that outcomes were heavily influenced by confounds (e.g., G4U2's disruptive behavior, G5U1's emotional posts), and Section 6 itself reports only minute stance shifts. With n=5, the success pattern in §5.3 is post hoc and not quantified. Please either provide a systematic within-case comparison that connects chatbot-use patterns to outcome measures (for example, per-round suspicion votes alongside chatbot interaction counts and qualitative evidence of causal influence) or explicitly downgrade the effectiveness claims to exploratory observations suitable for hypothesis generation.
minor comments (6)
  1. [§6] The first sentence of Section 6 contains a typo: 'a inherent limitation' should be 'an inherent limitation'.
  2. [Table 4] In the Disinformer row for 'Mitigate risk of detection', the text reads 'as as deceptive'; the duplicated 'as' should be removed.
  3. [§5.2.2] The sentence 'Following up with with "what is this year"' contains a doubled 'with'; please correct it.
  4. [Figures 5–7] The bar charts would be easier to evaluate if the exact counts per group were printed on the bars or reproduced in an appendix table, since the aggregate percentages currently conceal the per-participant distribution that is critical to the 'strategist' claim.
  5. [§1] The 'sword and shield' metaphor is not introduced until Section 5; a brief anticipatory mention in the introduction would help readers map the central metaphor onto the paper's structure.
  6. [§3.1.2] The phrase 'the game has five phases in each round' could be clarified by stating the total duration (2 hours 30 minutes) and the per-phase durations in Figure 1, since the figure does not include numeric durations in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning found: the study's claims are empirical observations from gameplay data, not derivations from fitted parameters or self-referential prior results.

full rationale

The paper's central claims are ethnographic and qualitative: it reports how participants in a Werewolf-inspired communication game used an LLM chatbot, and it groups these uses into categories such as 'Chatbot as strategist' (Section 4.5, Table 4). The 'strategic advisor' finding is presented as an observed pattern in participant prompts and interview statements, with direct quotes such as G5D's role-playing prompt in Section 5.1.1. No equation, fitted parameter, or formal derivation is involved, so the claim cannot reduce to its own inputs by construction. The only self-citation in the reference list is [65], Lim and Perrault's prior fact-checking chatbot work, which is cited in Section 5.1.2 as related work on fact-checking services and is not load-bearing for any of this paper's novel findings. Concerns that the 'Chatbot as strategist' category may be broad, that no inter-rater reliability is reported, or that results may be driven by a small number of participants relate to evidentiary robustness and generalizability, not circularity. The paper explicitly acknowledges the role-play limitation in Section 6, stating that participants were non-professional actors and that checklist-driven actions 'may reduce the authenticity of participants' behaviours.' This is an external validity caveat, not a circular dependence. Since the findings are grounded in recorded forum interactions, chatbot prompts, think-aloud protocols, and interviews, and since no load-bearing step is equivalent to its input by definition or by self-citation, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numeric free parameters or invented entities are introduced. The study rests on domain assumptions about the Werewolf game as a model of online disinformation, the generalizability of role-play behavior, the representativeness of an uncensored open-source LLM, and the reliability of thematic coding.

assumptions (4)
  • domain assumption Werewolf game mechanics are a valid model of key disinformation dynamics in small online communities.
    Section 3.1 draws explicit parallels between game roles and social media actors, while acknowledging differences in scale, duration, and motivations. The validity of these parallels is assumed, not empirically established.
  • domain assumption Role-play behavior by amateur participants reflects how real disinformation actors and defenders would behave.
    Section 6 states that participants were non-professional actors and followed checklists, which may reduce authenticity. The transfer of observed strategies to real-world settings is assumed.
  • domain assumption The uncensored open-source LLM chosen for the study represents a realistic tool that malicious actors can access.
    Section 3.2 justifies the model as state-of-the-art among uncensored models, but its capabilities differ from both commercial models and future malicious tools, which affects generalizability.
  • domain assumption Reflexive thematic analysis by five researchers yields reliable and consistent themes.
    Section 4.1 describes a stepped approach but reports no inter-rater reliability statistics or coding audit trail, so the reproducibility of the identified themes is assumed.

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Cite this review

Pith. "Pith review of Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation." pith.science (2026). https://pith.science/paper/ZFX7LU4N

@misc{pith2026250607211,
  author       = {Pith},
  title        = {Pith review of: Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZFX7LU4N}},
  note         = {Machine review of arXiv:2506.07211}
}
read the original abstract

The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated and persuasive disinformation, yet they also hold promise for enhancing detection and mitigation strategies. This paper investigates the complex dynamics between LLMs and disinformation through a communication game that simulates online forums, inspired by the game Werewolf, with 25 participants. We analyse how Disinformers, Moderators, and Users leverage LLMs to advance their goals, revealing both the potential for misuse and combating disinformation. Our findings highlight the varying uses of LLMs depending on the participants' roles and strategies, underscoring the importance of understanding their effectiveness in this context. We conclude by discussing implications for future LLM development and online platform design, advocating for a balanced approach that empowers users and fosters trust while mitigating the risks of LLM-assisted disinformation.

Figures

Figures reproduced from arXiv: 2506.07211 by the authors.

Figure 1
Figure 1. There are five phases (in yellow) in a round, each with a specific duration. The game is played for four rounds; players [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Moderator’s checklist. Each role has a checklist to ensure some common minimal interactions on the platform and [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Moderator’s view of the forum platform. The Disinformer’s and User’s views are similar, but do not have the Reports [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Stepped approach to reflexive thematic analysis of our data from multiple sources. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Chatbot use case counts for the 5 Disinformers [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Chatbot use case counts for the 5 Moderators across [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Chatbot use case counts for the 15 Users across all [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: User’s checklist. Each role has a checklist to ensure some common minimal interactions on the platform and role [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]
Figure 9
Figure 9. Figure 9: Disinformer’s checklist. Each role has a checklist to ensure some common minimal interactions on the platform and [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.