Pith. sign in

REVIEW 4 major objections 5 minor 51 references

Mapping the Reddit Bot Ecosystem: Taxonomy and Evolution

T0 review · 4 major / 5 minor · reviewed 2026-07-31 · deepseek-v4-flash

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

Pith's one-line read Reddit's recognized bot population forms an ecosystem of 18 behavioral species, and it peaked around 2021 before declining.

desk verdict First population-level taxonomy of overt Reddit bots, with a plausible pre-API decline that is partly an artifact of vote-threshold censoring; worth reviewing carefully. read the letter →

arxiv 2607.23941 v1 pith:ZHZH3SMK submitted 2026-07-27 cs.SI cs.CYcs.HC

Qiusi Sun , Thomas Gaskin , Branko Blagojevic , Milena Tsvetkova This is my paper
classification cs.SIcs.CYcs.HC
keywords RedditbotsbottaxonomyecosystemmachinebehaviorclusteringanalysispopulationdynamicsAutoModeratoronlinecommunities
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

The paper tries to show that Reddit's community-recognized bots are not an undifferentiated crowd but a structured ecosystem with at least 18 behaviorally distinct types, and that this ecosystem has a measurable life cycle: rapid growth, a peak during the COVID-19 period, and a decline that began in early 2022, before Reddit's paid API changes. The decline applies to publicly visible, crowd-vetted bots; the authors themselves warn it does not prove overall automation on Reddit has fallen, because covert bots and non-posting automation are invisible to their data. What persists through the contraction is diversity: the 18 bot types mostly appeared by 2016, none has gone extinct as of 2026, and their relative proportions stay stable even as total numbers fall. The paper also documents increasing centralization, with Reddit's official AutoModerator now responsible for more activity than all other bots combined.

What carries the argument

The central object is the behavioral fingerprint: each bot is represented by 23 normalized features in four dimensions—temporal (mean inter-post time, hourly entropy, response-time variance), community (number of subreddits, subreddit specialization, trigger dependence, similarity to parent posts), linguistic (lexicon size, lexical diversity, sentiment), and semantic (13 macro topic frequencies derived from BERTopic/Sentence-BERT embeddings). Hierarchical clustering over these fingerprints, with k=18 chosen by silhouette scores, produces the taxonomy; co-posting networks built from one-mode projections of bipartite bot–subreddit graphs map the ecosystem's community structure and its change o

What would settle it

A platform-side audit that counts all automated accounts, including those that never post, post privately, or evade 'good bot' votes, would settle the population claim: if total automation was flat or rising after 2022, the paper's decline is a visibility artifact. Alternatively, re-clustering the same bots while excluding trigger-dependent and moderation bots would test robustness: if the 18 groups collapse, the taxonomy is not stable.

Watch

Extended reading notes

Core claim

Analyzing 3,389 crowd-voted Reddit bots through 33 behavioral features spanning temporal rhythms, community focus, linguistic style, and semantic topics, and clustering on 23 retained features, the authors identify 18 distinct bot archetypes: content-specialized types (technology and programming, gaming, politics, adult content), behavior-driven types (conversational, triggered response, meme), and infrastructural roles (platform governance, moderation support, on-demand utility). Longitudinal tracking shows bot account creation accelerating from 2017, active accounts peaking during the COVID-19 period, and activity declining from the start of 2022—before Reddit's April 2023 API announcement

Load-bearing premise

The 3,389 crowd-voted, publicly recognized bots are a faithful enough sample of Reddit's bot population that the 18-type structure and the early-2022 decline reflect real bot ecology rather than changing user recognition habits or the hiding of newer AI bots.

Editorial extensions

If this is right

  • The 18-type taxonomy gives researchers a shared, empirically grounded language for studying bot diversity on Reddit and a template for comparing automation on other platforms.
  • The documented decline beginning in early 2022 means the contraction of recognized bots cannot be blamed solely on Reddit's 2023 API pricing change; other factors were already at work.
  • Stable species diversity alongside shrinking numbers implies that ecological roles persist even as the population thins, suggesting a resilient functional core.
  • The rise of AutoModerator to dominant activity signals a structural shift from decentralized community-developed bots to centralized platform tooling, with potential trade-offs for innovation and vulnerability.
  • The temporary GPT-2 bot communities around 2021 illustrate how generative-AI advances create novel variants within existing ecological roles before contracting as conditions change.

Reading between the lines

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

  • A plausible reading the paper leaves implicit is that the post-2022 decline in recognized bots may reflect a migration of automation from visible to invisible forms, as newer LLM-based bots become harder for users to recognize; this can be tested with independent bot-detection applied to the same time window.
  • If the covert-bot undercount grew over time, the observed 'stable diversity' of the 18 recognized species may be a property of the recognition process rather than of the full bot population.
  • The AutoModerator centralization result suggests a fragility dynamic: a platform-managed single point of failure could disrupt moderation more severely than a distributed population of independent bots; a simulation-based robustness test would clarify this.
  • The taxonomy likely captures benevolent, long-lived, publicly disclosed automation; a complementary dataset of covert or ephemeral bots would likely add new cluster types and change the ecosystem-level conclusions.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents a population-level study of Reddit bots identified through crowdsourced 'good bot'/'bad bot' votes. The authors compile 3,389 bot accounts with full activity histories, extract 33 temporal, community, linguistic, and semantic features, reduce these to 23 features via PCA, and apply hierarchical clustering to obtain a taxonomy of 18 bot 'species'. They further analyze the temporal evolution of bot counts, activity, and species diversity from 2005 to 2025, claiming rapid growth peaking around the COVID-19 period, a decline beginning in 2022 before Reddit's API policy changes, and a remarkably stable diversity of bot types. The paper also examines co-posting networks and discusses ecological and evolutionary interpretations.

Significance. If the results hold, this would be the first comprehensive empirical taxonomy of Reddit bots and a valuable longitudinal description of an online bot ecosystem. The paper draws on a relatively large sample of overt, user-recognized bots and attempts to link behavioral features to functional roles, which is a useful contribution to the growing literature on machine behavior. The explicit discussion of limitations—including the exclusion of covert bots and the possibility of recognition bias—is commendable. However, the central temporal claims rest on a sample selection rule that introduces time-dependent censoring, and the paper does not provide code, data, or robustness checks to support the key findings. The abstract presents the decline as an established fact without the important caveats that appear later in the text. With appropriate sensitivity analyses and a more cautious framing, the paper could make a solid descriptive contribution, but in its current form the population-level claims are not fully supported.

major comments (4)
  1. [Data and Methods, vote threshold; Discussion, limitations] The ≥10-vote inclusion rule creates a time-dependent censoring problem. Bots active after ~2021 have had less time to accumulate the required votes before the botranks.com data collection (2020–23) and botrank.net (ongoing to 2026) were assembled. The paper itself acknowledges that the analyses 'mainly capture relatively long-living, active and benevolent bots.' Consequently, the observed decline in active bot counts and new bot accounts after 2022 may be an artifact of this right-censoring rather than a real population trend. The abstract states that 'bot numbers and activity expanded rapidly before peaking around the COVID-19 period, then started declining' without this caveat. To support the temporal claim, the authors should provide sensitivity analyses using lower vote thresholds (e.g., ≥1, ≥5), cohort-based analyses of vote accrual rates, or some explicit model of the censoring pro
  2. [Results, Fig. 1C and surrounding text] The paper states: 'However, this decline in commenting activity disappears if we account for AutoModerator, which today is responsible for more activity on the platform than the rest of the bot population combined.' This is a direct qualification of the abstract's claim that 'bot numbers and activity ... started declining even before Reddit's 2023 API policy changes.' If the commenting-activity decline is not robust to including the platform's largest official bot, the abstract overstates the result. Please clarify what 'account for AutoModerator' means (include, exclude, or control for) and explicitly reconcile this statement with the headline claim. At minimum, the abstract should be revised to state that the decline holds for the sampled non-AutoModerator bot population.
  3. [Data and Methods, hierarchical clustering] The 18-type taxonomy is derived from a single hierarchical clustering run, with k chosen by silhouette scores. The paper mentions that results were 'similar' to k-means but provides no quantitative comparison. There is no assessment of cluster stability (e.g., bootstrap resampling, subsampling, alternative linkage methods, or varying the number of features retained after PCA). The taxonomy underpins the '18 distinct bot types' and the 'stable diversity' claims, so some evidence that the clusters are reproducible and not an artifact of the specific algorithmic choices is essential. Additionally, the paper does not provide code or data, making it impossible for readers to verify the clustering or reproduce the taxonomy.
  4. [Data and Methods, BERTopic macro-domain grouping] The 13 macro-domain topic frequencies are constructed by manually grouping fine-grained BERTopic topics. This introduces a subjective layer into the semantic features. The paper does not report intercoder reliability, alternative groupings, or sensitivity analyses. Because the taxonomy and the interpretation of 'content-specialized' bot types rely on these semantic features, the authors should demonstrate that the conclusions are not sensitive to the particular manual grouping decisions.
minor comments (5)
  1. [Abstract and Results] The abstract says 'started declining even before Reddit's 2023 API policy changes', but the Results section (Fig. 1) shows the decline beginning in early 2022. This is consistent, but the phrase 'even before' could be interpreted as surprising; consider rewording to 'the decline began in 2022, predating the April 2023 API announcement.'
  2. [Data and Methods, Table 1] The description of Subreddit specialization says the Herfindahl–Hirschman Index is '0 if activity is equally distributed among 10+ subreddits', but the index formula sum of squared shares is positive for any distribution; the statement should clarify that it would be near zero, not exactly zero, for a uniform distribution over 10+ subreddits.
  3. [General formatting] There are numerous typographical artifacts such as 'T witter', 'V ariance', 'T o', and inconsistent spacing in the PDF. Please proofread for these issues before publication.
  4. [References] The references include URLs with access dates. Reference [42] and [43] describe 'botranks' and 'botrank' but the main text uses 'botranks.com' and 'botrank.net'. Ensure names are consistent. Also, reference [8] (Moltbook) is an arXiv preprint dated Feb 2026; please confirm it is publicly available and correctly cited.
  5. [Fig. 2] The t-SNE plot is noted to be stochastic, but the visual separation of clusters is not quantified. A metric such as silhouette width per cluster or a validation measure would help readers assess cluster cohesion. This is a presentation issue, not a central claim.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: taxonomy and temporal trends are descriptive summaries; minor self-citations are not load-bearing.

full rationale

The paper's derivation chain is observational rather than equation-level. The 18 bot types are produced by PCA and hierarchical clustering on 23 behavioral features, with the cluster count chosen by silhouette scores, not set to a predetermined target; the temporal claims are counts of selected bots' activity from archived histories, not outputs of a fitted model. I find no step where a prediction reduces by construction to an input parameter or where the taxonomy is assumed in order to derive the taxonomy. The main validity concern is the ≥10-vote sample-selection threshold, which can censor recently created bots and distort the post-2022 decline. However, the authors explicitly acknowledge this limitation in the Discussion: they write that they 'mainly capture relatively long-living, active and benevolent bots' and that the 'observed decline in the bot population should not be interpreted as evidence that automation on Reddit has necessarily decreased.' This is a stated sampling-bias caveat, not a concealed circularity. The self-citations ([17] and [10], both by Tsvetkova and colleagues) are background context about Wikipedia bots and machine behavior, not load-bearing inputs to the Reddit taxonomy. The coauthor-run data source [43] is a data provenance statement rather than an imported theorem. These minor self-references are non-load-bearing, so the paper is best described as having no significant circularity, with a score of 2 reflecting the minor self-citation and data-source overlap rather than any circular derivation.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The central claims are descriptive summaries of a crowd-labeled sample. No physical/mathematical free parameters are fitted, but several analyst-selected thresholds (vote count, number of clusters, feature retention, topic grouping) shape the taxonomy and trends. The key hidden assumption is representativeness of the crowd-identified sample.

free parameters (4)
  • Number of clusters k = 18
    Silhouette scores across candidate values of k; 'most informative choice'. Central to the claim of 18 distinct bot types.
  • Vote threshold for bot inclusion = 10 votes
    Accounts with at least ten total votes are kept, yielding 3,389 confirmed bots; alters sample composition and excludes covert bots.
  • PCA feature retention threshold = 23 features contributing 90.49% to two main PCs
    Data-dependent rule for dropping correlated variables; directly shapes the clustering input.
  • Macro-domain topic grouping = 13 macro domains
    Manual grouping of fine-grained BERTopic topics into 13 semantic domains; taxonomy labels depend on this grouping.
assumptions (5)
  • domain assumption Crowdsourced 'good bot'/'bad bot' votes are reliable indicators of bot status
    Dataset built from botranks.com and botrank.net vote tallies; no independent verification of bot status; covert bots systematically excluded.
  • domain assumption The sampled accounts are representative of the Reddit bot population
    The authors state the sample 'mainly capture[s] relatively long-living, active and benevolent bots'; temporal and diversity claims rely on this representativeness.
  • domain assumption The 33 hand-built features capture behaviorally meaningful differences between bot types
    Feature set is author-selected; clustering on these features defines the taxonomy, with no external validation that these features carve at natural joints.
  • domain assumption Archived Reddit data from Academic Torrents provides complete comment and submission histories
    Feature computation and activity trends assume completeness of the archived histories.
  • domain assumption Hierarchical clustering on PCA-reduced features yields stable clusters
    k=18 chosen by silhouette; robustness to k-means is mentioned, but no bootstrap or stability analysis is reported.
invented entities (1)
  • 18 bot 'species'/archetype labels
    purpose: Organize the 3,389 bots into discrete behavioral categories and track diversity over time.
    Labels (e.g., 'Platform Governance', 'Conversational') are assigned post hoc by inspecting median feature values of clusters; there is no external benchmark confirming these are natural kinds.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Mapping the Reddit Bot Ecosystem: Taxonomy and Evolution." pith.science (2026). https://pith.science/paper/ZHZH3SMK

@misc{pith2026260723941,
  author       = {Pith},
  title        = {Pith review of: Mapping the Reddit Bot Ecosystem: Taxonomy and Evolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZHZH3SMK}},
  note         = {Machine review of arXiv:2607.23941}
}
read the original abstract

Automated agents increasingly participate in online communities, yet their population structure and roles remain poorly understood. Using a dataset of 3,389 identified bots and their full activity histories, we construct a taxonomy of bot "species" on the news aggregation and social media platform Reddit based on temporal, community, linguistic, and semantic features. Clustering analysis reveals 18 distinct bot types spanning content-specialized, behavior-driven, and infrastructural roles such as moderation and utility support. In addition, temporal analysis shows that bot numbers and activity expanded rapidly before peaking around the COVID-19 period, then started declining even before Reddit's 2023 API policy changes. However, the overall diversity of bot species has remained remarkably stable. These findings suggest that online bot populations form evolving digital ecosystems.

Figures

Figures reproduced from arXiv: 2607.23941 by the authors.

Figure 1
Figure 1. Reddit bot accounts and activity over time. A) Number of newly created and last-seen bot accounts. B) Number of active bots and total number of active monthly users based on data from seo.ai [45]. C) Number of comments and submissions contributed by the bots, with and without Reddit’s official moderator bot AutoModerator. The callouts indicate relevant platform updates and external events that could explain the obse… view at source ↗
Figure 2
Figure 2. t-SNE plot of the 18 bot types identified by the hierarchical clustering, with typical feature values per cluster. The width of the bars in the table to the right corresponds to the median value of the feature for the cluster (values range from 0 to 1 for all features except for sentiment, for which they can range from −1 to 1); these results were used to assign the descriptive labels of the clusters. The colors of … view at source ↗
Figure 3
Figure 3. Population sizes and activity of the 18 Reddit bot types over time. A) Stacked area chart of active user accounts. B) Stacked area chart of submissions. C) Stacked area chart of comments. Discussion The study provides, to our knowledge, the first population-level characterization of community-recognized Reddit bots, trac￾ing the diversity and evolution of the Reddit bot ecosystem from 2005 to 2025. The findings reve… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Co-posting networks of Reddit bot types in 2019, 2021, 2022, and 2024. Edge weights represent the shared number of posts in a subreddit, summed over all common subreddits to which the two bots posted. Node sizes correspond to the total number of posts made on the platf…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

51 extracted references · 2 linked inside Pith

  1. [1]

    Shao, C.et al.The spread of low-credibility content by social bots.Nature Communications9,4787 (2018)

    2018

  2. [2]

    & Aral, S

    V osoughi, S., Roy, D. & Aral, S. The spread of true and false news online.Science359,1146–1151 (2018). Sun et al. 2026: Mapping the Reddit Bot Ecosystem 11

    2018

  3. [3]

    S., Samadi, M

    Kirilenko, A., Kyle, A. S., Samadi, M. & Tuzun, T. The flash crash: High-frequency trading in an electronic market. The Journal of Finance72,967–998 (2017)

    2017

  4. [4]

    T eutloff, O.et al.Winners and losers of generative AI: Early evidence of shifts in freelancer demand.Journal of Eco- nomic Behavior & Organization235,106845 (2025)

    2025

  5. [5]

    L., Strohkorb Sebo, S., Jung, M., Scassellati, B

    T raeger, M. L., Strohkorb Sebo, S., Jung, M., Scassellati, B. & Christakis, N. A. Vulnerable robots positively shape human conversational dynamics in a human-robot team.Proceedings of the National Academy of Sciences USA117, 6370–6375 (2020)

    2020

  6. [6]

    & Raymond, L

    Brynjolfsson, E., Li, D. & Raymond, L. Generative AI at work.Quarterly Journal of Economics140,889–942 (2025)

    2025

  7. [7]

    & Ponti, G

    Kestin, G., Miller, K., Klales, A., Milbourne, T. & Ponti, G. AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting.Scientific Reports15,17458 (2025)

    2025

  8. [8]

    Humans welcome to observe

    Jiang, Y., Zhang, Y., Shen, X., Backes, M. & Zhang, Y."Humans welcome to observe": A first look at the agent social net- work MoltbookarXiv:2602.10127 [cs.SI]. Feb. 2026. http://arxiv.org/abs/2602.10127

    arXiv 2026

Show all 51 references
  1. [9]

    Rahwan, I.et al.Machine behaviour.Nature568,477–486 (2019)

    2019

  2. [10]

    & W erner, T

    T svetkova, M., Y asseri, T., Pescetelli, N. & W erner, T. A new sociology of humans and machines.Nature Human Be- haviour8,1864–1876 (2024)

    2024

  3. [11]

    Backlinko T eam.Reddit User and Growth Stats (Updated)Dec. 2025. https://backlinko.com/reddit-users (2026)

    2025

  4. [12]

    & Naaman, M

    Lloyd, T., Reagle, J. & Naaman, M. ‘There has to be a lot that we’re missing’: Moderating AI-generated content on Reddit.Proceedings of the ACM on Human-Computer Interaction9,1–24 (2025)

    2025

  5. [13]

    Hurtado, S., Ray, P. & Marculescu, R.Bot detection in Reddit political discussioninProceedings of the Fourth Interna- tional W orkshop on Social Sensing(Association for Computing Machinery, New Y ork, NY, USA, Apr. 2019), 30–35

    2019

  6. [14]

    L.Contested play: The culture and politics of Reddit botsinSocialbots and Their FriendsNum Pages: 18 (Routledge, 2016)

    Massanari, A. L.Contested play: The culture and politics of Reddit botsinSocialbots and Their FriendsNum Pages: 18 (Routledge, 2016)

    2016

  7. [15]

    & Lalor, J

    Ma, M.-C. & Lalor, J. P.An empirical analysis of human-bot interaction on RedditinProceedings of the Sixth W ork- shop on Noisy User-generated Text (W-NUT 2020)(Association for Computational Linguistics, Nov. 2020), 101–106

    2020

  8. [16]

    D.Typologies and Taxonomies: An Introduction to Classification Techniques(SAGE, 1994)

    Bailey, K. D.Typologies and Taxonomies: An Introduction to Classification Techniques(SAGE, 1994)

    1994

  9. [17]

    & Y asseri, T

    T svetkova, M., García-Gavilanes, R., Floridi, L. & Y asseri, T. Even good bots fight: The case of Wikipedia.PLoS ONE 12,e0171774 (2017)

    2017

  10. [18]

    Unpacking the social media bot: A typology to guide research and policy

    Gorwa, Robert and Guilbeault, Douglas. Unpacking the social media bot: A typology to guide research and policy. Policy Internet12,225–248 (2020)

    2020

  11. [19]

    & Menczer, F

    Y ang, K.-C., Ferrara, E. & Menczer, F. Botometer 101: Social bot practicum for computational social scientists.Journal of Computational Social Science5,1511–1528 (2022)

    2022

  12. [20]

    BotUmc: An uncertainty-aware Twitter bot detection with multi-view causal inferencearXiv:2503.03775 [cs]

    Y ang, T.et al. BotUmc: An uncertainty-aware Twitter bot detection with multi-view causal inferencearXiv:2503.03775 [cs]. 2025. http://arxiv.org/abs/2503.03775

    2025 arXiv

  13. [21]

    & Ioannidis, S.BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipelinearXiv:2306.00037 [cs]

    Shevtsov, A., Antonakaki, D., Lamprou, I., Pratikakis, P. & Ioannidis, S.BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipelinearXiv:2306.00037 [cs]. Apr. 2025. http://arxiv.org/abs/2306. 00037

    2025 arXiv

  14. [22]

    Kim, T., Shin, H., Hwang, H. J. & Jeong, S. Posting bot detection on blockchain-based social media platform using machine learning techniques.Proceedings of the International AAAI Conference on W eb and Social Media15,303–314 (2021)

    2021

  15. [23]

    Shah, H. G. & Joshi, H. Spam bot detection on T witter platform using positional attention based dense convolu- tional neural network.Applied Soft Computing184,113725 (2025). Sun et al. 2026: Mapping the Reddit Bot Ecosystem 12

    2025

  16. [24]

    Deshmukh, A., Moh, M. & Moh, T.-S.Bot detection in social media using GraphSage and BERTin2024 IEEE/WIC International Conference on W eb Intelligence and Intelligent Agent Technology (WI-IAT)(IEEE, Bangkok, Thailand, Dec. 2024), 804–811

    2024

  17. [25]

    & Saracco, F

    Caldarelli, G., De Nicola, R., Del Vigna, F., Petrocchi, M. & Saracco, F. The role of bot squads in the political propa- ganda on T witter.Communications Physics3,81 (2020)

    2020

  18. [26]

    & Menczer, F

    Y ang, K.-C., T orres-Lugo, C. & Menczer, F. Prevalence of low-credibility information on T witter during the COVID- 19 outbreak.W orkshop Proceedings of the 14th International AAAI Conference on W eb and Social Media2020,16 (2020)

    2020

  19. [27]

    A., Janssen, J., Orji, R

    Alipour, S. A., Janssen, J., Orji, R. & Zincir-Heywood, N.Lightweight early-warning bot detection on X (Twitter): Temporal patterns and entropy insightsin2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC)(IEEE, T oronto, ON, Canada, July 2025), 250–255

    2025

  20. [28]

    & Şahin, S.A review on social bot detection techniques and research directionsinProceedings of the Interna- tional Security and Cryptology Conference(Turkey, 2017), 156–161

    Karataş, A. & Şahin, S.A review on social bot detection techniques and research directionsinProceedings of the Interna- tional Security and Cryptology Conference(Turkey, 2017), 156–161

    2017

  21. [29]

    P., Savage, S

    Seering, J., Flores, J. P., Savage, S. & Hammer, J. The social roles of bots: Evaluating impact of bots on discussions in online communities.Proceedings of the ACM on Human-Computer Interaction2,157:1–157:29 (2018)

    2018

  22. [30]

    & Mueen, A.DeBot: Twitter bot detection via warped correlationin2016 IEEE 16th Inter- national Conference on Data Mining (ICDM)(IEEE, Barcelona, Spain, Dec

    Chavoshi, N., Hamooni, H. & Mueen, A.DeBot: Twitter bot detection via warped correlationin2016 IEEE 16th Inter- national Conference on Data Mining (ICDM)(IEEE, Barcelona, Spain, Dec. 2016), 817–822

    2016

  23. [31]

    & Jajodia, S

    Chu, Z., Gianvecchio, S., W ang, H. & Jajodia, S. Detecting automation of twitter accounts: Are you a human, bot, or cyborg?IEEE Transactions on Dependable and Secure Computing9,811–824 (2012)

    2012

  24. [32]

    Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A. & T esconi, M.The paradigm-shift of social spambots: Evidence, theories, and tools for the arms raceinProceedings of the 26th International Conference on W orld Wide W eb Compan- ion - WWW ’17 Companion(ACM Press, Perth, ...

    2017

  25. [33]

    A., V arol, O., Ferrara, E., Flammini, A

    Davis, C. A., V arol, O., Ferrara, E., Flammini, A. & Menczer, F.BotOrNotinProceedings of the 25th International Conference Companion on W orld Wide W eb - WWW 16 Companion(International W orld Wide W eb Conferences Steering Committee, Montréal Québec Canada, Apr. 2016), 273–274

    2016

  26. [34]

    P., Kagan, V

    Dickerson, J. P., Kagan, V . & Subrahmanian, V .Using sentiment to detect bots on Twitter: Are humans more opinion- ated than bots?in2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)(IEEE, China, Aug. 2014), 620–627

    2014

  27. [35]

    & Flammini, A

    Ferrara, E., V arol, O., Davis, C., Menczer, F. & Flammini, A. The rise of social bots.Communications of the ACM59, 96–104 (2016)

    2016

  28. [36]

    Ng, L. H. X. & Carley, K. M. BotBuster: Multi-platform bot detection using a mixture of experts.Proceedings of the International AAAI Conference on W eb and Social Media17,686–697 (2023)

    2023

  29. [37]

    Peng, H.et al.Unsupervised social bot detection via structural information theory.ACM Transactions on Informa- tion Systems42,1–42 (2024)

    2024

  30. [38]

    & Ferrara, E

    Pozzana, I. & Ferrara, E. Measuring bot and human behavioral dynamics.Frontiers in Physics8(2020)

    2020

  31. [39]

    A social bot recognition method combing emojis informationApr

    W ang, X.et al. A social bot recognition method combing emojis informationApr. 2024. https://www.researchsquare. com/article/rs-4223128/v1 (2025)

    2024

  32. [40]

    Geiger, R. S. & Halfaker, A.When the levee breaks: Without bots, what happens to Wikipedia’s quality control pro- cesses?inProceedings of the 9th International Symposium on Open Collaboration(Association for Computing Machin- ery, New Y ork, NY, USA, Aug. 2013), 1–6

    2013

  33. [41]

    Geiger, R. S. & Halfaker, A. Operationalizing conflict and cooperation between automated software agents in Wikipedia.Proceedings of the ACM on Human-Computer Interaction1,1–33 (2017). Sun et al. 2026: Mapping the Reddit Bot Ecosystem 13

    2017

  34. [42]

    McFarlin, B.Botranks(Archived repository). Dec. 2023. https://github.com/Brandawg93/Botranks (2025)

    2023

  35. [43]

    Blagojevic, B.B0tRank - Reddit Bot Ranking & Analysis Platformhttps://botrank.net (2026)

    2026

  36. [44]

    T rujillo, M.et al. When the echo chamber shatters: Examining the use of community-specific language post-subreddit ban inProceedings of the 5th W orkshop on Online Abuse and Harms (WOAH 2021)(eds Mostafazadeh Davani, A.et al.) (Association for Computational Linguistics, Online...

    2021

  37. [45]

    SEO.AI.How Many Users Does Reddit Have? Statistics & Facts (2025)Feb. 2025. https://seo.ai/blog/how-many-users- does-reddit-have (2026)

    2025

  38. [46]

    & Bruckman, A

    Jhaver, S., Birman, I., Gilbert, E. & Bruckman, A. Human-machine collaboration for content regulation: The case of Reddit automoderator.ACM Transactions on Computer-Human Interaction26,31:1–31:35 (2019)

    2019

  39. [47]

    ‘Unethical’ AI research on Reddit under fire.Science388,570–571 (2025)

    O’Grady, C. ‘Unethical’ AI research on Reddit under fire.Science388,570–571 (2025)

    2025

  40. [48]

    AI slop is ruining Reddit for everyone.Wired(2025)

    T enbarge, K. AI slop is ruining Reddit for everyone.Wired(2025)

    2025

  41. [49]

    Gupta, A.‘Reddit is for humans’: Social media platform may soon ask to see your face to keep AI bots outMar. 2026. https://www.livemint.com/technology/tech-news/reddit-is-for-humans-social-media-platform-may-soon-ask-to- see-your-face-to-keep-ai-bots-out-11774156396396.html (2026)

    2026

  42. [50]

    & W agner, C

    Assenmacher, D., Fröhling, L. & W agner, C. Y ou are a bot! – Studying the development of bot accusations on T wit- ter.Proceedings of the International AAAI Conference on W eb and Social Media18,113–125 (2024)

    2024

  43. [51]

    u/botname

    Geiger, R. S.The lives of botsinCritical Point of View: A Wikipedia Reader(eds Lovink, G. & Tkacz, N.) 78–93 (In- stitute of Network Cultures, Amsterdam, 2011). Sun et al. 2026: Mapping the Reddit Bot Ecosystem 14 Supplementary Information 0 5 0 1000 2000 Mean log inter-post t...

    2011

Pith tools

Reviewed July 31, 2026 · model on record in the stance chip above.