Pith. sign in

REVIEW 4 major objections 6 minor 53 references

A user's pattern of actions—not their words—reliably flags IRA-linked troll accounts on Reddit, beating content-based detection.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Behavioral policies inferred from Reddit activity detect IRA-linked troll accounts with 94.9% median macro-F1, beating a 91.2% text-embedding baseline and degrading gracefully under noise and hijacking.

T0 review reviewed 2026-08-03 challenge →

load-bearing objection Strong empirical benchmark with a clean early-detection result, but the headline F1 gap is confounded by a subreddit-sampling mismatch that the paper doesn't address. the 4 major comments →

arxiv 2602.02838 v2 pith:SQZTXXYH submitted 2026-02-02 cs.SI cs.LG

Beyond Content: Behavioral Policies Reveal Actors in Information Operations

Philipp J. Schneider , Lanqin Yuan , Marian-Andrei Rizoiu This is my paper
classification cs.SI cs.LG
keywords information operationsinverse reinforcement learningtroll detectionbehavioral policyMarkov decision processRedditInternet Research Agency
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper sets out to show that how a person acts on a social platform carries a stable signal of coordinated manipulation, independent of what they post. Using 12,064 Reddit users (99 linked to the Russian Internet Research Agency) and 38 million activity steps, it models each user's behavior as a sequence of states and actions, infers a behavioral policy, and classifies trolls versus ordinary users. The best policy-based classifier reaches a median macro-F1 of 94.9%, compared with 91.2% for a strong text-embedding baseline, with non-overlapping confidence intervals. Policy features also detect trolls from as few as three actions and degrade gracefully under noise or account hijacking. If right, this matters because content and network signals are becoming less reliable as generative text improves and platforms restrict data access.

Core claim

The central claim is that behavioral policy—the conditional distribution over platform actions given engagement context—is a more stable and discriminative signature of malicious actors than the content they produce. On Reddit's 2015-2018 activity window, the paper converts each user's history into a Markov decision process with 12 states (initial or engaged interactions, receiving replies, with agreement polarity) and 6 actions (create thread, root comment, reply agree/neutral/disagree, wait), then infers per-user policies by empirical frequencies, GAIL, and maximum-entropy deep inverse reinforcement learning. Feeding these policies to a classifier separates IRA-linked trolls from ordinary

What carries the argument

The central object is the behavioral policy: for each user, a probability distribution over six platform actions conditioned on a twelve-way context state, encoding how the user decides at each step (create a thread, post a root comment, reply agreeing/neutral/disagreeing, or wait for a reply). The paper casts each user's activity log as a Markov decision process, infers a policy by three routes—empirical state-action frequencies, Generative Adversarial Imitation Learning (GAIL), and maximum-entropy deep inverse reinforcement learning—and feeds the resulting policy representation into a random-forest or gradient-boosting classifier. The state-action structure is what carries the argument: it

Load-bearing premise

The load-bearing premise is that the systematic behavioral differences between the two user pools reflect coordinated manipulation rather than ordinary differences in subreddit habits, activity volume, or account age—and that the 12-state/6-action encoding captures a real policy rather than incidental platform mechanics.

What would settle it

Rerun the troll-versus-organic classification with a negative set matched one-to-one on subreddit, posting volume, and account age; if the policy advantage over text embeddings collapses or the intervals overlap, the signal is population difference, not manipulation.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Malicious accounts can be flagged from activity logs alone, without reading their posts, on this campaign; behavior outperforms a strong text-embedding baseline and holds up across classifiers.
  • Detection is possible early: with three state-action pairs the empirical-policy classifier reaches 91.4% macro-F1, so intervention can start before an account builds a long footprint.
  • Behavioral features degrade smoothly—staying above 80% macro-F1 with up to 50% of actions randomly replaced—so they remain usable on noisy or incomplete platform data.
  • Trolls are behaviorally heterogeneous: cluster analysis finds thread-creators, root-commenters, and a minority whose policies resemble organics, implying that a single behavioral profile cannot catch every actor.
  • Combining short behavioral summaries with text and human review would strengthen practical detection, since policy features reduce variance and catch signals text misses.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if the policy signal reflects operational goals rather than platform quirks, the same state-action encoding could transfer to other pseudonymous platforms such as X or TikTok, but only after redefining actions to match each platform's interface; a direct test is to train on a second platform's transparency-report labels.
  • Editorial inference: behavioral timing is harder for generative models to fake than text style, so policy features may stay informative even as LLM-generated content saturates platforms; one could simulate this by generating troll-like text with LLMs while keeping observed human posting rhythms and measuring how much detection accuracy remains.
  • Editorial inference: the three consistently misclassified accounts suggest a ceiling for behavior-only detection—actors who adopt reply-heavy, conversational patterns can hide in plain sight; pairing policy features with metadata such as account age or coordinated timing across accounts would likely recover some of them.
  • Editorial inference: because the positive and negative user pools differ in subreddit mix and activity volume, part of the reported gap may be population difference rather than manipulation; a matched negative set would clarify the size of the true behavioral effect.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes detecting IRA-linked troll accounts on Reddit from behavioral policies inferred from state-action trajectories, rather than text content. Using 99 labeled troll accounts and 11,965 organic users from the authors' prior dataset, it compares three policy representations (empirical state-action frequencies, GAIL, maximum-entropy deep IRL) against ModernBERT text embeddings, reporting median macro-F1 of 94.9% (GAIL) vs 91.2% (embeddings), with advantages at short traces and under random perturbation. It also clusters troll policies into three subgroups and simulates account hijacking. The manuscript includes detailed appendices on hyperparameter searches and code availability.

Significance. If the comparison is valid, this is a useful contribution: it demonstrates that simple, content-free behavioral features can be competitive with or better than strong text embeddings for a real-world IO campaign, and the robustness/early-detection results are practically relevant. The paper is transparent about data construction, includes extensive hyperparameter tables, and provides code. However, the central interpretation hinges on the troll/organic contrast being manipulation-specific; the unmatched sampling frames and the lack of ablations against simpler behavioral features leave this unresolved. The paper is worth publishing after the confound is addressed and the robustness claims are recalibrated.

major comments (4)
  1. [Materials and Methods/Dataset; Sec. 2.2/Fig. 2a] The headline comparison is between two differently sampled populations. The positive class is 99 IRA accounts from Reddit's transparency report with ≥10 interactions; Table A1 shows their activity is concentrated in a handful of subreddits (r/uncen1776, r/Bad_Cop_No_Donut, r/CryptoCurrency, r/copwatch, r/The_Donald). The negative class is drawn from 15 news/politics/rights/identity subreddits by activity rank in [49], then restricted to 2015–2018. The state/action encoding contains no subreddit feature—states encode only initial/engaged status and reply polarity—so subreddit-specific conversation norms (thread-creation vs reply-heavy communities) are absorbed into the 'behavioral policy'. Truncating organic trajectories to troll lengths does not address this. Because the central claim is that behavioral dynamics, not content, reveal manipulation, the classifier may instead be separating
  2. [Sec. 2.2/Fig. 2b; Materials and Methods/State and action space] The term 'behavioral policy' overstates what is currently demonstrated. The encoding has 12 states and 6 actions, so the empirical policy in Eq. (1) is a normalized 72-dimensional state-action frequency table. The 3-action result may be largely explainable by the marginal tendency of trolls to start with 'create thread' (the text itself makes this observation). No baseline using the marginal action distribution (6 bins), a first-order action Markov model, or standard temporal features (inter-event times, hour-of-day) is reported. Without such ablations, it is unclear whether the state-conditioned policy adds information beyond simpler behavioral summaries. Please add these baselines to support the 'decision process' interpretation.
  3. [Appendix B.4-B.5; Sec. 2.2] The hyperparameter tables report that configurations were used to 'guide optimal model selection', but the paper does not describe a nested cross-validation or a validation split. If hyperparameters were selected using test-fold performance, the reported median F1s and percentiles are optimistically biased. Please specify the model-selection protocol (e.g., inner CV on training folds only) and report the selected configuration and its held-out performance. This is load-bearing for the exact numerical headline (94.9% vs 91.2%), though not necessarily for the qualitative ranking.
  4. [Sec. 2.3/Fig. 3c] The hijacking-robustness claim is stronger than the data support. At η=10%, the policy median is 90.1% with 5th-95th percentiles 83.0-93.0, while the content median is 88.1% with percentiles 86.0-89.4; the intervals overlap substantially, so 'performs better' is not supported. At η=50%, the medians reverse (86.1 vs 86.6). Please report paired or bootstrap CIs on the difference, or soften the conclusion to 'comparable' under this synthetic attack model.
minor comments (6)
  1. [Fig. 3c, Fig. B7] Typo: 'Poliy-based' should be 'Policy-based'.
  2. [Materials and Methods/Content Embeddings] State explicitly how per-item ModernBERT embeddings were aggregated into a user-level feature for Fig. 2. Only the hijacking scenario defines a mean embedding.
  3. [Sec. 2.3] The 'matched sample of 99 organic users' in the cluster analysis is not defined; specify matching variables (e.g., total action count, account age).
  4. [Table B7] The row beginning '10.956±0.0150.939±0.008' appears malformed; likely '0.956'.
  5. [Appendix A, Table A1] The first data row for r/uncen1776 appears to list only a percentage without a count; please align formatting.
  6. [Materials and Methods/Dataset] Clarify which components of the pipeline are inherited from [49] (organic sampling and the state/action encoding) and which are new to this paper, so the novelty boundary is unambiguous.

Circularity Check

0 steps flagged

Empirical benchmark with inherited state/action encoding and organic pool from the authors' prior work; no prediction reduces to its own input by construction.

full rationale

This paper is an empirical comparison, not a derivation chain whose output equals its input. User trajectories are encoded as state-action sequences; policies are inferred by empirical normalization, GAIL, or max-entropy IRL; and the resulting features are classified under stratified cross-validation. The headline result (GAIL median macro-F1 94.9% vs text embedding 91.2%) is a measured out-of-sample comparison, not a quantity forced by the fitted features. The main self-reference is the systematic reuse of the authors' prior work [49]: the organic user pool is "the dataset developed in [49]"; the Reddit state/action encoding is "the encoding introduced in Yuan et al. [49], which we adopt"; and the agreement labels that split reply actions and states are obtained by fine-tuning DeBERTa-v3 "Following [49]". These are borrowed components, and the classification target comes from Reddit's 2017 transparency report, an external label source; the claim does not reduce to the citation. The paper itself flags that labels "reflect the platform's internal detection heuristics and disclosure practices," and the unmatched sampling frames of trolls (subreddit distribution concentrated in r/uncen1776, r/CryptoCurrency, r/copwatch, etc.) versus organics (15 news/politics/rights/identity subreddits from [49]) are validity threats; however, those are sample-mismatch concerns, not circular reasoning. No fitted parameter is renamed as a prediction, no uniqueness theorem from the authors' prior work is invoked to force the choice, and no equation in the paper reduces to its own input. At most, the self-citation dependence is a minor transparency/reproducibility issue, so circularity is low.

Axiom & Free-Parameter Ledger

6 free parameters · 5 axioms · 0 invented entities

The central claim is an empirical classifier benchmark, so the ledger is dominated by model-selection hyperparameters and sampling assumptions rather than theory axioms. No invented entities: the 'policy' is a standard MDP/IRL construct. The most consequential free choices are the GAIL/IRL grid-search parameters (Tables B2-B7), selected by macro-F1 on this dataset; the 10-interaction troll inclusion threshold; the organic trajectory truncation; and the k=3 clustering solution. The state/action space itself is imported from the authors' own prior work [49], and the ground-truth labels come from Reddit's transparency report, whose heuristics the paper acknowledges as incomplete.

free parameters (6)
  • GAIL hyperparameters (learning rate 3e-4, entropy coeff 0.01, discount gamma in {0.95,0.99}, reward-net width d_h in {4, = best config per Table B7 (e.g., gamma=0.99, d_h=8, 5k steps, RN=1: XGB F1 0.956)
    Grid search in Table B7; headline GAIL 94.9% reflects selection on the target metric.
  • MaxEnt IRL hyperparameters (learning rate in {0.005,0.01,0.05}, epochs in {500,1000,1500}, gamma in {0.9,0.95}, entropy = best configs per Tables B3-B5 (e.g., alpha=0.05, gamma=0.95, 1000 epochs, lambda1=1.0, lambda2=1.0: RF F1 0.941)
    Selected by RF/XGB macro-F1 on the same dataset used for the reported numbers.
  • Soft value iteration temperature = not reported
    Mentioned in the MaxEnt IRL methods; the value is not specified, which affects the sharpness of the recovered policy.
  • Troll inclusion threshold (minimum interactions) = 10
    Filters 147 active transparency-report accounts down to 99; changes the composition of the positive class and the measured F1.
  • Organic trajectory truncation to match troll lengths = not specified exactly
    Fig 2a caption note; the matching procedure is not detailed, and truncation changes the information available to the content baseline.
  • Number of troll clusters k = 3
    Chosen by elbow and silhouette (Fig B8); the cluster interpretation (thread-creators vs root-commenters vs organic-like) is post hoc.
axioms (5)
  • domain assumption User behavior satisfies the Markov property: the next state depends only on the current state and action
    Stated in Policy Inference; it is the formal premise that makes 'policy' well-defined, but social-media behavior plausibly has longer memory.
  • domain assumption The 12-state / 6-action encoding from Yuan et al. [49] captures behaviorally meaningful decision structure on Reddit
    Adopted in Materials and Methods; authored by the same group, so its discriminative power is not independently established.
  • domain assumption Reddit's 2017 transparency-report labels are correct ground truth
    The paper itself notes labels reflect the platform's internal detection heuristics and may be incomplete or skewed (Discussion limitations).
  • domain assumption The organic pool from [49] is representative of ordinary Reddit users
    Sampled from 15 news/politics/rights/identity subreddits by activity quartile; not a random sample of Reddit.
  • domain assumption Environment transition kernel P is known and fixed; unobservable actions (e.g., voting) do not affect the trajectory model
    Materials and Methods: the environment excludes unobservable actions and assumes the public activity log is complete.

reviewed 2026-08-03 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Beyond Content: Behavioral Policies Reveal Actors in Information Operations." pith.science (2026). https://pith.science/paper/SQZTXXYH

@misc{pith2026260202838,
  author       = {Pith},
  title        = {Pith review of: Beyond Content: Behavioral Policies Reveal Actors in Information Operations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SQZTXXYH}},
  note         = {Machine review of arXiv:2602.02838}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features. These approaches are increasingly brittle as generative models produce convincing text, platforms restrict access to behavioral data, and actors migrate to less-regulated spaces. We introduce a platform-agnostic framework that identifies malicious actors from their behavioral policies by modeling user activity as sequential decision processes. We apply this approach to 12,064 Reddit users, including 99 accounts linked to the Russian Internet Research Agency in Reddit's 2017 transparency report, analyzing over 38 million activity steps from 2015-2018. Activity-based representations, which model how users act rather than what they post, consistently outperform content models in detecting malicious accounts. When distinguishing trolls -- users engaged in coordinated manipulation -- from ordinary users, policy-based classifiers achieve a median macro-F1 of 94.9\%, compared to 91.2\% for text embeddings. Policy features also enable earlier detection from short traces and degrade more gracefully under evasion strategies or data corruption. These findings show that behavioral dynamics encode stable, discriminative signals of manipulation on Reddit's IRA-linked campaign, and point to resilient detection strategies in the era of synthetic content and limited data access.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

53 extracted references · 1 linked inside Pith

  1. [1]

    Arora and P

    S. Arora and P. Doshi. A survey of inverse reinforcement learning: Challenges, methods and progress.Artificial Intelligence, 297:103500, 2021

    2021

  2. [2]

    C. A. Bail, B. Guay, E. Maloney, A. Combs, D. S. Hillygus, F. Merhout, D. Freelon, and A. Volfovsky. Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017.Proceedings of the National Academy of Sciences, 117(1):243–250, 2020

    2017

  3. [3]

    Baumgartner, S

    J. Baumgartner, S. Zannettou, B. Keegan, M. Squire, and J. Blackburn. The Pushshift Reddit dataset. InProceedings of the International AAAI Conference on Web and Social Media, volume 14, pp. 830–839. AAAI Press, 2020

    2020

  4. [4]

    Bellutta and K

    D. Bellutta and K. M. Carley. Investigating coordinated account creation using burst detection and network analysis.Journal of Big Data, 10(1):20, 2023

    2023

  5. [5]

    Bursztein, B

    E. Bursztein, B. Benko, D. Margolis, T. Pietraszek, A. Archer, A. Aquino, A. Pit- sillidis, and S. Savage. Handcrafted fraud and extortion: Manual account hijacking in the wild. InProceedings of the 2014 Conference on Internet Measurement Conference, pp. 347–358, 2014

    2014

  6. [6]

    Cinelli, S

    M. Cinelli, S. Cresci, W. Quattrociocchi, M. Tesconi, and P. Zola. Coordinated inauthentic behavior and information spreading on Twitter.Decision Support Systems, 160:113819, 2022

    2022

  7. [7]

    Cinus, M

    F. Cinus, M. Minici, L. Luceri, and E. Ferrara. Exposing cross-platform coordi- nated inauthentic activity in the run-up to the 2024 U.S. election. InProceedings of the ACM on Web Conference 2025, pp. 541–559, 2025

    2024

  8. [8]

    S. Cresci. A decade of social bot detection.Communications of the ACM, 63(10):72–83, 2020

    2020

  9. [9]

    Del Vicario, A

    M. Del Vicario, A. Bessi, F. Zollo, F. Petroni, A. Scala, G. Caldarelli, H. E. Stan- ley, and W. Quattrociocchi. The spreading of misinformation online.Proceedings of the National Academy of Sciences, 113(3):554–559, 2016

    2016

  10. [10]

    Elmas, R

    T. Elmas, R. Overdorf, A. F. ¨Ozkalay, and K. Aberer. Ephemeral astroturfing attacks: The case of fake Twitter trends. In2021 IEEE European Symposium on Security and Privacy, pp. 403–422. IEEE, 2021

    2021

  11. [11]

    Geissler and S

    D. Geissler and S. Feuerriegel. Analyzing the strategy of propaganda using inverse reinforcement learning: Evidence from the 2022 Russian invasion of Ukraine. InCompanion Publication of the 2024 Conference on Computer Supported Cooperative Work and Social Computing, New York, NY, 2024. ACM

    2022

  12. [12]

    J. A. Goldstein, J. Chao, S. Grossman, A. Stamos, and M. Tomz. How persuasive 20 is AI-generated propaganda?PNAS Nexus, 3(2):pgae034, 2024

    2024

  13. [13]

    Gonz´ alez-Bail´ on and M

    S. Gonz´ alez-Bail´ on and M. De Domenico. Bots are less central than verified accounts during contentious political events.Proceedings of the National Academy of Sciences, 118(11):e2013443118, 2021

    2021

  14. [14]

    Grinberg, K

    N. Grinberg, K. Joseph, L. Friedland, B. Swire-Thompson, and D. Lazer. Fake news on Twitter during the 2016 US presidential election.Science, 363(6425):374– 378, 2019

    2016

  15. [15]

    Hasher, D

    L. Hasher, D. Goldstein, and T. Toppino. Frequency and the conference of refer- ential validity.Journal of Verbal Learning and Verbal Behavior, 16(1):107–112, 1977

    1977

  16. [16]

    Ho and S

    J. Ho and S. Ermon. Generative adversarial imitation learning.Advances in Neural Information Processing Systems, 29, 2016

    2016

  17. [17]

    Horta Ribeiro, H

    M. Horta Ribeiro, H. Hosseinmardi, R. West, and D. J. Watts. Deplatforming did not decrease Parler users’ activity on fringe social media.PNAS Nexus, 2(3):pgad035, 2023

    2023

  18. [18]

    Information Operations

    Joint Chiefs of Staff. Information Operations. Joint Publication JP 3-13, Joint Chiefs of Staff, Department of Defense, Washington, DC, Nov. 2012

    2012

  19. [19]

    J. L. Juul and J. Ugander. Comparing information diffusion mechanisms by matching on cascade size.Proceedings of the National Academy of Sciences, 118(46):e2100786118, 2021

    2021

  20. [20]

    Q. Kong, P. Calderon, R. Ram, O. Boichak, and M.-A. Rizoiu. Interval-censored transformer Hawkes: Detecting information operations using the reaction of social systems. InProceedings of the ACM Web Conference 2023, pp. 1813–1821, 2023

    2023

  21. [21]

    D. M. Lazer, M. A. Baum, Y. Benkler, A. J. Berinsky, K. M. Greenhill, F. Menczer, M. J. Metzger, B. Nyhan, G. Pennycook, D. Rothschild, et al. The science of fake news.Science, 359(6380):1094–1096, 2018

    2018

  22. [22]

    D. L. Linvill and P. L. Warren. Troll factories: Manufacturing specialized disinformation on Twitter.Political Communication, 37(4):447–467, 2020

    2020

  23. [23]

    Lorenz, H

    J. Lorenz, H. Rauhut, F. Schweitzer, and D. Helbing. How social influence can undermine the wisdom of crowd effect.Proceedings of the National Academy of Sciences, 108(22):9020–9025, 2011

    2011

  24. [24]

    Luceri, S

    L. Luceri, S. Giordano, and E. Ferrara. Detecting troll behavior via inverse reinforcement learning: A case study of Russian trolls in the 2016 US election. InProceedings of the International AAAI Conference on Web and Social Media, volume 14, pp. 417–427. AAAI Press, 2020

    2016

  25. [25]

    Luceri, V

    L. Luceri, V. Pant` e, K. Burghardt, and E. Ferrara. Unmasking the web of deceit: Uncovering coordinated activity to expose information operations on Twitter. In Proceedings of the ACM on Web Conference 2024, pp. 2530–2541, 2024

    2024

  26. [26]

    McInnes, J

    L. McInnes, J. Healy, N. Saul, and L. Großberger. UMAP: Uniform manifold approximation and projection.Journal of Open Source Software, 3(29):861, 2018

    2018

  27. [27]

    A. Y. Ng and S. J. Russell. Algorithms for inverse reinforcement learning. In Proceedings of the Seventeenth International Conference on Machine Learning, pp. 663–670, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc

    2000

  28. [28]

    Pacheco, A

    D. Pacheco, A. Flammini, and F. Menczer. Unveiling coordinated groups behind white helmets disinformation. InCompanion Proceedings of the Web Conference 21 2020, pp. 611–616, 2020

    2020

  29. [29]

    Pacheco, P.-M

    D. Pacheco, P.-M. Hui, C. Torres-Lugo, B. T. Truong, A. Flammini, and F. Menczer. Uncovering coordinated networks on social media: Methods and case studies. InProceedings of the International AAAI Conference on Web and Social Media, volume 15, pp. 455–466, 2021

    2021

  30. [30]

    Pennycook, T

    G. Pennycook, T. D. Cannon, and D. G. Rand. Prior exposure increases per- ceived accuracy of fake news.Journal of Experimental Psychology: General, 147(12):1865, 2018

    2018

  31. [31]

    Pfeffer, D

    J. Pfeffer, D. Matter, and A. Sargsyan. The half-life of a tweet.Proceedings of the International AAAI Conference on Web and Social Media, 17(1):1163–1167, 2023

    2023

  32. [32]

    M. Pote, T. Elmas, A. Flammini, and F. Menczer. Coordinated reply attacks in influence operations: Characterization and detection. InProceedings of the International AAAI Conference on Web and Social Media, volume 19, pp. 1586– 1598, 2025

    2025

  33. [33]

    Pougu´ e-Biyong, V

    J. Pougu´ e-Biyong, V. Semenova, A. Matton, R. Han, A. Kim, R. Lambiotte, and D. Farmer. DEBAGREEMENT: A comment-reply dataset for (dis)agreement detection in online debates. InNeural Information Processing Systems, 2021

    2021

  34. [34]

    Rizoiu, L

    M.-A. Rizoiu, L. Xie, S. Sanner, M. Cebrian, H. Yu, and P. Van Hentenryck. Expecting to be HIP: Hawkes intensity processes for social media popularity. InProceedings of the 26th International Conference on World Wide Web, pp. 735–744, 2017

    2017

  35. [35]

    X. Ruan, Z. Wu, H. Wang, and S. Jajodia. Profiling online social behaviors for compromised account detection.IEEE Transactions on Information Forensics and Security, 11(1):176–187, 2016

    2016

  36. [36]

    S. Russell. Learning agents for uncertain environments (extended abstract). InProceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 101–103, New York, NY, 1998. ACM

    1998

  37. [37]

    Sayyadiharikandeh, O

    M. Sayyadiharikandeh, O. Varol, K.-C. Yang, A. Flammini, and F. Menczer. Detection of novel social bots by ensembles of specialized classifiers. InProceed- ings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2725–2732, 2020

    2020

  38. [38]

    P. J. Schneider and M.-A. Rizoiu. The effectiveness of moderating harmful online content.Proceedings of the National Academy of Sciences, 120(34):e2307360120, 2023

    2023

  39. [39]

    C. Shao, G. L. Ciampaglia, O. Varol, K.-C. Yang, A. Flammini, and F. Menczer. The spread of low-credibility content by social bots.Nature Communications, 9(1):4787, 2018

    2018

  40. [40]

    Spitale, N

    G. Spitale, N. Biller-Andorno, and F. Germani. AI model GPT-3 (dis) informs us better than humans.Science Advances, 9(26):eadh1850, 2023

    2023

  41. [41]

    Starbird, A

    K. Starbird, A. Arif, and T. Wilson. Disinformation as collaborative work: Sur- facing the participatory nature of strategic information operations.Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–26, 2019

    2019

  42. [42]

    R. S. Sutton, A. G. Barto, et al.Reinforcement learning: An introduction. MIT Press, 1998. 22

    1998

  43. [43]

    Tardelli, L

    S. Tardelli, L. Nizzoli, M. Tesconi, M. Conti, P. Nakov, G. Da San Martino, and S. Cresci. Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.Proceedings of the National Academy of Sciences, 121(20):e2307038121, 2024

    2024

  44. [44]

    Thomas, F

    K. Thomas, F. Li, C. Grier, and V. Paxson. Consequences of connectivity: Char- acterizing account hijacking on Twitter. InProceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 489–500, 2014

    2014

  45. [45]

    van der Maaten and G

    L. van der Maaten and G. Hinton. Visualizing data using t-SNE.Journal of Machine Learning Research, 9(86):2579–2605, 2008

    2008

  46. [46]

    Vosoughi, D

    S. Vosoughi, D. Roy, and S. Aral. The spread of true and false news online. Science, 359(6380):1146–1151, 2018

    2018

  47. [47]

    Warner, A

    B. Warner, A. Chaffin, B. Clavi´ e, O. Weller, O. Hallstr¨ om, S. Taghadouini, A. Gal- lagher, R. Biswas, F. Ladhak, T. Aarsen, G. T. Adams, J. Howard, and I. Poli. Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference. InProceedings of the 63rd Annual Meeting of the Associati...

    2025

  48. [48]

    Wulfmeier, P

    M. Wulfmeier, P. Ondruska, and I. Posner. Maximum entropy deep inverse reinforcement learning.arXiv preprint arXiv:1507.04888, 2015

    Pith/arXiv arXiv 2015

  49. [49]

    L. Yuan, P. J. Schneider, and M.-A. Rizoiu. Behavioral homophily in social media via inverse reinforcement learning: A Reddit case study. InProceedings of the ACM on Web Conference 2025, pp. 576–589, 2025

    2025

  50. [50]

    Zannettou, T

    S. Zannettou, T. Caulfield, E. De Cristofaro, M. Sirivianos, G. Stringhini, and J. Blackburn. Disinformation warfare: Understanding state-sponsored trolls on Twitter and their influence on the web. InCompanion Proceedings of the 2019 World Wide Web Conference, pp. 218–226, 2019

    2019

  51. [51]

    Zannettou, M

    S. Zannettou, M. Sirivianos, J. Blackburn, and N. Kourtellis. The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. Journal of Data and Information Quality, 11(3):1–37, 2019

    2019

  52. [52]

    B. D. Ziebart, A. L. Maas, J. A. Bagnell, A. K. Dey, et al. Maximum entropy inverse reinforcement learning. InProceedings of the AAAI Conference on Artificial Intelligence, volume 8, pp. 1433–1438. AAAI Press, 2008. Code Availability Code, and instructions for obtaining the source data, are available at: https://anonymous.4open.science/r/behavioral-dynami...

    2008

  53. [53]

    We evaluate two distance metrics: standard Euclidean distance and the symmet- ric weighted KL-divergence (SWKL) recently introduced in [49]

    andUniform Manifold Approximation and Projection(UMAP) [26]—to visualize the pairwise distances between users based on their empirical policies. We evaluate two distance metrics: standard Euclidean distance and the symmet- ric weighted KL-divergence (SWKL) recently introduced in [49]. These metrics are applied to the action distributions inferred from eac...

This paper was first reviewed by deepseek-v4-flash on August 3, 2026.