Pith. sign in

REVIEW 4 major objections 7 minor 75 references

A Graph Neural Architecture Search Approach for Identifying Bots in Social Media

T0 review · 4 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read This paper claims that an evolutionary search over message-passing layouts in a graph neural network detects Twitter bots at 85.7% accuracy, beating published state-of-the-art detectors.

desk verdict An honest application of DFG-NAS to bot detection, but the headline accuracy is the best of five test-set runs, so the surpassing-SOTA claim is not established as written. read the letter →

arxiv 2411.16285 v1 pith:77WDD2PG submitted 2024-11-25 cs.LG cs.SI

Georgios Tzoumanekas , Michail Chatzianastasis , Loukas Ilias , George Kiokes , John Psarras , Dimitris Askounis This is my paper · ORCID
classification cs.LGcs.SI
keywords botdetectiongraphneuralnetworksarchitecturesearchpropagationtransformationsocialmediaplatformXTwiBot-20evolutionaryalgorithm
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 tries to establish that bot detection on X (formerly Twitter) can be improved by automating the design of a graph neural network instead of fixing it by hand. It adapts the evolutionary architecture search method DFG-NAS to relational graph convolutional networks, searching over sequences of propagation and transformation operations in the message-passing pipeline. On the TwiBot-20 dataset, with a graph of 229,580 users linked by follow relationships and enriched with user metadata, the best architecture found reaches 85.7% test accuracy, which the authors report as surpassing published state-of-the-art models. Ablations show that each metadata modality, a gating mechanism, and skip connections each contribute to that performance.

What carries the argument

The load-bearing machinery is the search space of propagation (P) and transformation (T) operations that make up the message-passing protocol of a relational graph convolutional network. P aggregates messages from a node's neighbors with relation-specific weights; T applies a nonlinear transformation to node representations. DFG-NAS represents an architecture as a sequence of P and T steps and evolves it with four mutations (append P, append T, replace P with T, replace T with P), evaluating each candidate on validation accuracy. To keep deep pipelines trainable, P connections use a gating mechanism and T connections use skip connections. The search selects a P/T sequence that feeds a final MLP classifying each user as bot or human.

What would settle it

Run the strongest published baselines on exactly the 70/20/10 split and preprocessing used in this paper; if any of them reaches or exceeds 85.7% accuracy, the claim of surpassing state-of-the-art is not established. A second check: replace the evolutionary search with random sampling of propagation/transformation sequences under identical training; if random architectures match the searched accuracy, the search itself is not the source of the gain.

Watch

Extended reading notes

Core claim

The central claim is that an automatically searched message-passing configuration outperforms hand-designed detectors on a standard bot-detection benchmark. The authors construct a heterogeneous graph whose nodes are users and whose edges encode follower/following relations, and each node carries metadata from the user's description, tweets, numerical properties, and categorical properties. They adapt DFG-NAS's evolutionary search to choose the order and number of propagation (neighbor aggregation) and transformation (node update) operations inside relational graph convolutional layers, with gating on propagation and skip connections on transformation. The five highest-validation architectures from the search all land near 85% test accuracy, and the best reaches 85.7% accuracy, 87.1% F1-score, and 0.712 Matthews correlation coefficient, higher than the published numbers of the state-of-the-art models they compare against on the same dataset. The paper's ablations attribute part of this gain to the gating and skip-connection mechanisms and to the full set of metadata features.

Load-bearing premise

The comparison to earlier detectors relies entirely on their published accuracy numbers and assumes those numbers came from the same data split, preprocessing, and evaluation protocol as the authors' 70/20/10 split, since no baseline is rerun in the same pipeline.

Editorial extensions

If this is right

  • If the central claim holds, bot detection on graph-structured social data can be framed as a search problem, so adapting to a new dataset need not require manually redesigning the message-passing architecture.
  • The top five searched architectures achieve validation accuracies between 86.8% and 87.0%, suggesting a plateau of near-optimal propagation/transformation configurations rather than a single fragile design.
  • Removing the gate from propagation lowers accuracy by about 0.5 percentage points, and removing skip connections from transformation lowers it by about 0.93 points, so both mechanisms contribute to the searched architecture's result.
  • The feature ablations imply the reported edge depends on having all four metadata modalities; dropping categorical properties alone drops accuracy to 79.2%, and single-feature models perform markedly worse.

Reading between the lines

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

  • If the baseline comparison survives a common evaluation protocol, the practical consequence is that architecture search can absorb much of the manual design work in GNN-based bot detection, making detectors easier to refresh as bots evolve.
  • The ablation ranking of user features suggests a next step is letting the search choose how to fuse modalities rather than concatenating them; the paper itself lists multimodal fusion as future work.
  • A direct test of whether the search, rather than model capacity, drives the gain would compare the evolved P/T sequence against randomly sampled or fixed one-block architectures trained under the same budget.
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 / 7 minor

Summary. The paper applies DFG-NAS, an evolutionary neural architecture search method, to relational graph convolutional networks for bot detection on the TwiBot-20 dataset. It encodes user descriptions, tweets, and numerical/categorical properties, builds a heterogeneous graph from follower/following relations, and searches over sequences of propagation (P) and transformation (T) operations. The five architectures with highest validation accuracy are retrained and evaluated; the best one reaches 85.68% accuracy, which the authors report as surpassing state-of-the-art baselines. Ablations examine removing metadata features and removing the gate and skip-connection mechanisms.

Significance. If the claim were established, the paper would provide a useful demonstration that evolutionary architecture search can replace hand-designed message-passing stacks in relational GNNs for bot detection, while also showing the value of graph structure and metadata. The paper's own results include five-run means and standard deviations for the proposed architectures, and the limitations section candidly notes the single-dataset evaluation and absence of hyperparameter tuning. However, the central quantitative claims depend on a test-set-selected architecture and on copied baseline numbers, and the ablation results do not support the stated conclusion about feature necessity; these issues currently prevent the significance from being realized.

major comments (4)
  1. [Section VII, Table IV] Section VII states that after retraining the five architectures, "the third architecture achieves the best evaluation metrics" and is designated "our model, since it provides the highest accuracy." The reported 85.68% is therefore the maximum over five test-set evaluations, not the accuracy of an architecture chosen by validation alone. Because the five candidates were shortlisted on validation and then the best test performer was selected, the headline estimate is upward-biased by selection. This is load-bearing: the abstract and conclusion assert superiority over state-of-the-art on the basis of this number. Please report the validation-selected architecture's test accuracy (the first row in Figure 3) as the primary result, or average over the five candidates, or provide a multiple-testing correction and significance test.
  2. [Section VI.A, Table V] Section VI.A says "We are using the published results for the comparison," and Table V lists baseline metrics without error bars. The authors' own numbers are five-run means on their 70/20/10 split, with their own preprocessing and training budget. There is no evidence that the published baseline results were obtained under the same split, preprocessing, or evaluation protocol. The "surpassing state-of-the-art" claim is therefore not statistically established. Please rerun at least the strongest baselines (BotRGCN, SATAR) under the identical protocol and report their variability, or explicitly state that the comparison is approximate and weaken the claim.
  3. [Section VI.A, Section III.A, Table V] The paper's contribution is the automatic search over P/T sequences, but no fixed-architecture RGCN is trained with the same metadata, graph, split, and training settings as a control. Without such a control, the gains over published fixed-architecture results could be due to the feature encoding, training protocol, or random split rather than to the searched architecture itself. Please add a same-pipeline controlled comparison, e.g., BotRGCN's default architecture trained under the same 70/20/10 split and 100-epoch protocol. Additionally, Section III.A states that Ilias et al. [29] "conducted their experiments on the Cresci'17 dataset," yet Table V reports a TwiBot-20 result for [29]; if the published value in that row comes from a different dataset, the row is not a valid baseline for this comparison.
  4. [Section VIII, Table VI] Section VIII concludes that "all features contribute to the model's performance," but Table VI shows that removing the description gives accuracy 0.859 ± 0.004, F1 0.875 ± 0.004, and MCC 0.718 ± 0.008, and removing the numerical properties gives accuracy 0.859 ± 0.003 and MCC 0.716 ± 0.007, both numerically above the full model's 0.857 ± 0.004 / 0.712 ± 0.007. With overlapping standard deviations, this ablation does not support the claim of necessity for those features; at most it shows that the categorical features are important. Please add significance testing or reinterpret the results.
minor comments (7)
  1. [Section IV and throughout] The dataset name is written inconsistently as "TwiBot-20", "Twibot-20", and "Twi-Bot20"; please standardize it.
  2. [Section V.B, Eq. (5)] The transformation formula appears to contain only self/relation-dependent terms with no explicit neighbor aggregation; please clarify how it differs from propagation in Eq. (4) and whether the same relation weights W_r are shared across both equations.
  3. [Section VI.B, Table IV] The text gives training budgets for the search (70 epochs) and final training (100 epochs), but the tables say "five runs of results are averaged"; please state in the text that five independent runs were used for the final evaluation and whether the search was performed once or multiple times.
  4. [Section VII] The sentence "In Figure 2, the five architectures ... are depicted" should refer to Figure 3; Figure 2 is the layer-connection schematic from Section V.
  5. [Section VIII, gate ablation] The text "the architecture without the gate has a reduced accuracy by 0.5%" does not match Table VIII (0.857 vs 0.853, i.e., 0.4 percentage points); please align the text and table.
  6. [Section VIII, skip-connection ablation] The sentence "We see that the architecture without the gate has a reduced accuracy" should read "without the skip-connection"; the current sentence repeats the gate ablation.
  7. [Section VI.A] The reference list entry [12] is cited as "BotRGCN et al. [12]" in the text; it should be "Feng et al. [12]".

Circularity Check

1 steps flagged · score 6.0 of 10

The reported 85.7% accuracy is the maximum over five test-set evaluations, selected after inspecting test results, so the headline 'surpassing SOTA' number is a test-set-selected estimate rather than an independent evaluation.

  1. fitted input called prediction [Section VII (Results), Tables IV and V; Abstract]
    "Upon closer examination of the results, the third architecture achieves the best evaluation metrics. ... From now on we will refer to the third architecture as our model, since it provides the highest accuracy."

    The architecture called 'our model' is chosen after looking at the five test-set results, and the number reported as 'ours' in Table V (0.8568) is the test accuracy of that same architecture. The abstract then presents this as a single model's accuracy ('achieve an accuracy of 85.7%, surpassing state-of-the-art models'). By the paper's own protocol, the reported value is max(test_acc_1, ..., test_acc_5): the test set was used both to select the architecture and to score it. This is a fitted-input-called-prediction pattern—the discrete model choice is fit to the test labels, and the same test labels are then reported as the model's predicted performance.

full rationale

The core NAS-to-RGCN derivation is otherwise self-contained: the P/T operations are standard RGCN message-passing equations (4)-(5), the search follows the external DFG-NAS algorithm [13], and the preprocessing follows external BotRGCN [12]. The baselines in Table V are published numbers from the BotRGCN paper, not recomputed in this pipeline, so the comparison involves protocol-mismatch risk but not circularity. The one load-bearing circular step is the test-set-based selection of 'our model' in Section VII: the architecture is labeled 'our model, since it provides the highest accuracy' after all five test outcomes are known, and the same test outcome is then reported as its accuracy. The ablation study additionally contains a factual inconsistency (removing description or numerical features gives higher accuracy than the full model in Table VI, yet the text claims the full model is best), but that is a reporting/correctness problem, not circularity. The paper's own Limitations section acknowledges single-dataset experiments, no hyperparameter tuning, and concatenative fusion; these further reduce confidence in generalization but do not alter the circularity finding. Overall, the central surpassing-SOTA claim rests on a number that is by construction the maximum over five test-set evaluations, hence partial circularity.

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

The central claim depends on the TwiBot-20 benchmark labels and graph, on the comparability of published baseline numbers, and on hand-chosen search and training hyperparameters. No new theoretical entity is invented; the method is assembled from published components. The most fragile input is the unverified comparability of baselines and the stochastic search budget.

free parameters (4)
  • NAS search budget = k=15, T=80, 70 epochs per candidate
    Chosen under GPU limits; the architectures found and the final result depend on this budget.
  • Final training protocol = 100 epochs, Adam lr=1e-3
    Hand-set and not tuned; reported metrics come from this final retraining.
  • Search-phase optimizer settings = Adam lr=0.04, weight decay 2e-4, dropout 0.5/0.8
    Hand-chosen hyperparameters; the paper states no hyperparameter tuning was performed.
  • Data split = 70/20/10
    Fixed by the authors; published baseline results may come from a different split or protocol.
assumptions (4)
  • domain assumption TwiBot-20 labels and follower/following edges are reliable ground truth for bot detection.
    All evaluation and architecture selection depends on the correctness of this benchmark.
  • domain assumption Published baseline results in Table V are directly comparable despite being generated outside this paper's pipeline.
    This is the load-bearing premise for the surpassing state-of-the-art claim; Section VI.A says published results are used.
  • domain assumption A short evolutionary search with k=15 and T=80 yields architectures whose validation accuracy predicts held-out test performance.
    The method's entire design relies on this proxy; the final selected architectures are then retrained and tested.
  • domain assumption RoBERTa embeddings and RGCN message passing preserve enough user information for the detection task.
    The user representation and graph propagation are assumed to encode useful signals for distinguishing bots from humans.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Graph Neural Architecture Search Approach for Identifying Bots in Social Media." pith.science (2026). https://pith.science/paper/77WDD2PG

@misc{pith2026241116285,
  author       = {Pith},
  title        = {Pith review of: A Graph Neural Architecture Search Approach for Identifying Bots in Social Media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/77WDD2PG}},
  note         = {Machine review of arXiv:2411.16285}
}
read the original abstract

Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.

Figures

Figures reproduced from arXiv: 2411.16285 by the authors.

Figure 1
Figure 1. Model used for Bot detection. User metadata is fed to the architecture proposed by NAS. The P step includes message aggregation from neighbour [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Example of connections between the layers of NAS architecture. New [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Permutations of Propagation (P) and Transformation (T) functions [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

75 extracted references · 71 canonical work pages

  1. [29]

    Mul- timodal detection of bots on x (twitter) using transformers

    Loukas Ilias, Ioannis Michail Kazelidis, and Dimitris Askounis. Mul- timodal detection of bots on x (twitter) using transformers. IEEE Transactions on Information Forensics and Security , 19:7320–7334, 2024

    work page 2024

  2. [1]

    Multitask learning for recognizing stress and depression in social media

    Loukas Ilias and Dimitris Askounis. Multitask learning for recognizing stress and depression in social media. Online Social Networks and Media, 37-38:100270, 2023

    work page 2023

  3. [2]

    Calibration of transformer-based models for identifying stress and depression in social media

    Loukas Ilias, Spiros Mouzakitis, and Dimitris Askounis. Calibration of transformer-based models for identifying stress and depression in social media. IEEE Transactions on Computational Social Systems , 11(2):1979–1990, 2024

    work page 1979

  4. [3]

    Depression detection in social media posts using transformer-based models and auxiliary features

    Marios Kerasiotis, Loukas Ilias, and Dimitris Askounis. Depression detection in social media posts using transformer-based models and auxiliary features. Social Network Analysis and Mining , 14:196, 2024

    work page 2024

  5. [4]

    Izzat Alsmadi and Michael J. O’Brien. How many bots in russian troll tweets? Information Processing & Management , 57(6):102303, 2020

    work page 2020

  6. [5]

    Moffitt, and Kathleen M

    Joshua Uyheng, J.D. Moffitt, and Kathleen M. Carley. The language and targets of online trolling: A psycholinguistic approach for social cybersecurity. Information Processing & Management , 59(5):103012, 2022

    work page 2022

  7. [6]

    How social bots can influence public opinion more effectively: Right connection strategy

    Yaozeng Zhang, Jing Ma, and Fanshu Fang. How social bots can influence public opinion more effectively: Right connection strategy. Physica A: Statistical Mechanics and its Applications , 633:129386, 2024

    work page 2024

  8. [7]

    Being my own gatekeeper, how i tell the fake and the real – fake news perception between typologies and sources

    Yi Xu, Deru Zhou, and Wei Wang. Being my own gatekeeper, how i tell the fake and the real – fake news perception between typologies and sources. Information Processing & Management , 60(2):103228, 2023

    work page 2023

Show all 75 references
  1. [8]

    How does social media knowledge help in combating fake news? testing a structural equation model

    Yantian Mi and Oberiri Destiny Apuke. How does social media knowledge help in combating fake news? testing a structural equation model. Thinking Skills and Creativity , page 101492, 2024. 13

    2024

  2. [9]

    Seven months with the devils: A long-term study of content polluters on twitter

    Kyumin Lee, Brian Eoff, and James Caverlee. Seven months with the devils: A long-term study of content polluters on twitter. Proceedings of the International AAAI Conference on Web and Social Media , 5(1):185– 192, Aug. 2021

    2021

  3. [10]

    Social fingerprinting: detection of spambot groups through dna-inspired behavioral modeling

    Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spog- nardi, and Maurizio Tesconi. Social fingerprinting: detection of spambot groups through dna-inspired behavioral modeling. IEEE Transactions on Dependable and Secure Computing , page 1–1, 2017

    2017

  4. [11]

    An efficient reinforcement learning-based botnet detection approach

    Mohammad Alauthman, Nauman Aslam, Mouhammd Al-kasassbeh, Suleman Khan, Ahmad Al-Qerem, and Kim-Kwang Raymond Choo. An efficient reinforcement learning-based botnet detection approach. Journal of Network and Computer Applications , 150:102479, 2020

    2020

  5. [12]

    Botrgcn: Twitter bot detection with relational graph convolutional networks

    Shangbin Feng, Herun Wan, Ningnan Wang, and Minnan Luo. Botrgcn: Twitter bot detection with relational graph convolutional networks. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining , ASONAM ’21, page 236–239. ACM, 11 2021

    2021

  6. [13]

    Deep and flexible graph neural architecture search

    Wentao Zhang, Zheyu Lin, Yu Shen, Yang Li, Zhi Yang, and Bin Cui. Deep and flexible graph neural architecture search. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Ma...

    2022

  7. [14]

    Twibot-20: A comprehensive twitter bot detection benchmark

    Shangbin Feng, Herun Wan, Ningnan Wang, Jundong Li, and Minnan Luo. Twibot-20: A comprehensive twitter bot detection benchmark. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management , CIKM ’21, page 4485–4494, New York, NY , USA, 2021. A...

    2021

  8. [15]

    Social bots distort the 2016 u.s

    Alessandro Bessi and Emilio Ferrara. Social bots distort the 2016 u.s. presidential election online discussion. First Monday, 21, 11 2016

    2016

  9. [16]

    What types of covid-19 conspiracies are populated by twitter bots? First Monday, 5 2020

    Emilio Ferrara. What types of covid-19 conspiracies are populated by twitter bots? First Monday, 5 2020

    2020

  10. [17]

    Dialog in the echo chamber: Fake news framing predicts emotion, argumentation and dialogic social knowledge building in subsequent online discussions

    Christian Scheibenzuber, Laurentiu-Marian Neagu, Stefan Ruseti, Benedikt Artmann, Carolin Bartsch, Montgomery Kubik, Mihai Dascalu, Stefan Trausan-Matu, and Nicolae Nistor. Dialog in the echo chamber: Fake news framing predicts emotion, argumentation and dialogic social knowle...

    2023

  11. [18]

    Cyberbullying detection for low-resource languages and dialects: Re- view of the state of the art

    Tanjim Mahmud, Michal Ptaszynski, Juuso Eronen, and Fumito Masui. Cyberbullying detection for low-resource languages and dialects: Re- view of the state of the art. Information Processing & Management , 60(5):103454, 2023

    2023

  12. [19]

    Rosgas: Adaptive social bot detection with reinforced self-supervised gnn architecture search

    Yingguang Yang, Renyu Yang, Yangyang Li, Kai Cui, Zhiqin Yang, Yue Wang, Jie Xu, and Haiyong Xie. Rosgas: Adaptive social bot detection with reinforced self-supervised gnn architecture search. ACM Transactions on the Web , 17(3):1–31, May 2023

    2023

  13. [20]

    Deep neural networks for bot detection

    Sneha Kudugunta and Emilio Ferrara. Deep neural networks for bot detection. Information Sciences, 467:312–322, October 2018

    2018

  14. [21]

    Twitter bot detection using bidirec- tional long short-term memory neural networks and word embeddings

    Feng Wei and Uyen Trang Nguyen. Twitter bot detection using bidirec- tional long short-term memory neural networks and word embeddings. In 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) , pages 101– 109, 2019

    2019

  15. [22]

    Rethinking graph neural architecture search from message-passing

    Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, and Qingming Huang. Rethinking graph neural architecture search from message-passing. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 6653–6662, 2021

    2021

  16. [23]

    Botornot: A system to evaluate social bots

    Clayton Allen Davis, Onur Varol, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. Botornot: A system to evaluate social bots. In Proceedings of the 25th International Conference Companion on World Wide Web, WWW ’16 Companion, page 273–274, Republic and Canton of Genev...

    2016

  17. [24]

    Botometer 101: Social bot practicum for computational social scientists

    Kai-Cheng Yang, Emilio Ferrara, and Filippo Menczer. Botometer 101: Social bot practicum for computational social scientists. Journal of Computational Social Science , 5(2):1511–1528, 2022

    2022

  18. [25]

    Twitter bot detection using diverse content features and applying ma- chine learning algorithms

    Fawaz Khaled Alarfaj, Hassaan Ahmad, Hikmat Ullah Khan, Abdul- lah Mohammaed Alomair, Naif Almusallam, and Muzamil Ahmed. Twitter bot detection using diverse content features and applying ma- chine learning algorithms. Sustainability, 15(8), 2023

    2023

  19. [26]

    Bot-mgat: A transfer learning model based on a multi-view graph attention network to detect social bots

    Eiman Alothali, Motamen Salih, Kadhim Hayawi, and Hany Alashwal. Bot-mgat: A transfer learning model based on a multi-view graph attention network to detect social bots. Applied Sciences, 12(16), 2022

    2022

  20. [27]

    Satar: A self-supervised approach to twitter account representation learning and its application in bot detection

    Shangbin Feng, Herun Wan, Ningnan Wang, Jundong Li, and Minnan Luo. Satar: A self-supervised approach to twitter account representation learning and its application in bot detection. In Proceedings of the 30th ACM International Conference on Information; Knowledge Man- agement...

    2021

  21. [28]

    Detecting malicious activity in twitter using deep learning techniques

    Loukas Ilias and Ioanna Roussaki. Detecting malicious activity in twitter using deep learning techniques. Applied Soft Computing , 107:107360, 2021

    2021

  22. [30]

    Twitter bot detection using neural networks and linguistic embeddings

    Feng Wei and Uyen Trang Nguyen. Twitter bot detection using neural networks and linguistic embeddings. IEEE Open Journal of the Computer Society, 4:218–230, 2023

    2023

  23. [31]

    Multi-view co- attention network for fake news detection by modeling topic-specific user and news source credibility

    Parisa Bazmi, Masoud Asadpour, and Azadeh Shakery. Multi-view co- attention network for fake news detection by modeling topic-specific user and news source credibility. Information Processing & Manage- ment, 60(1):103146, 2023

    2023

  24. [32]

    Botartist: Twitter bot detection ma- chine learning model based on twitter suspension

    Alexander Shevtsov, Despoina Antonakaki, Ioannis Lamprou, Polyvios Pratikakis, and Sotiris Ioannidis. Botartist: Twitter bot detection ma- chine learning model based on twitter suspension. arXiv preprint arXiv:2306.00037, 2023

    2023 arXiv

  25. [33]

    Twitter bot detection and ranking using supervised machine learning models

    K Sujith, Shreya Chowdhury, Arsh Goyal, Anand Vardhan Hegde, and Ramamoorthy Srinath. Twitter bot detection and ranking using supervised machine learning models. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), volume 01, pages 1–6, 2022

    2022

  26. [34]

    Botmoe: Twitter bot detection with community- aware mixtures of modal-specific experts

    Yuhan Liu, Zhaoxuan Tan, Heng Wang, Shangbin Feng, Qinghua Zheng, and Minnan Luo. Botmoe: Twitter bot detection with community- aware mixtures of modal-specific experts. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Re...

  27. [35]

    A statistical approach for reducing misinformation propagation on twitter social media

    Naman Saxena, Adwitiya Sinha, Tanishk Bansal, and Ankita Wadhwa. A statistical approach for reducing misinformation propagation on twitter social media. Information Processing & Management , 60(4):103360, 2023

    2023

  28. [36]

    Caleb: A conditional adversarial learning framework to enhance bot detection

    Ilias Dimitriadis, George Dialektakis, and Athena Vakali. Caleb: A conditional adversarial learning framework to enhance bot detection. Data & Knowledge Engineering , 149:102245, 2024

    2024

  29. [37]

    Scalable and generalizable social bot detection through data selec- tion

    Kai-Cheng Yang, Onur Varol, Pik-Mai Hui, and Filippo Menczer. Scalable and generalizable social bot detection through data selec- tion. Proceedings of the AAAI Conference on Artificial Intelligence , 34(01):1096–1103, April 2020

    2020

  30. [38]

    Real-time bot infection detection system using dns fingerprinting and machine-learning

    Vicente Quezada, Fabian Astudillo-Salinas, Luis Tello-Oquendo, and Paul Bernal. Real-time bot infection detection system using dns fingerprinting and machine-learning. Computer Networks, 228:109725, 2023

    2023

  31. [39]

    Twitter spammer detection using data stream clustering

    Zachary Miller, Brian Dickinson, William Deitrick, Wei Hu, and Alex Hai Wang. Twitter spammer detection using data stream clustering. Information Sciences, 260:64–73, 2014

    2014

  32. [40]

    Debot: Twitter bot detection via warped correlation

    Nikan Chavoshi, Hossein Hamooni, and Abdullah Mueen. Debot: Twitter bot detection via warped correlation. In 2016 IEEE 16th International Conference on Data Mining (ICDM) , pages 817–822, 2016

    2016

  33. [41]

    Botwalk: Efficient adaptive exploration of twitter bot networks

    Amanda Minnich, Nikan Chavoshi, Danai Koutra, and Abdullah Mueen. Botwalk: Efficient adaptive exploration of twitter bot networks. In 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , pages 467–474, 2017

    2017

  34. [42]

    Mannocci, S

    L. Mannocci, S. Cresci, A. Monreale, A. Vakali, and M. Tesconi. Mulbot: Unsupervised bot detection based on multivariate time series. In 2022 IEEE International Conference on Big Data (Big Data) , pages 1485–1494, Los Alamitos, CA, USA, dec 2022. IEEE Computer Society

    2022

  35. [43]

    Twitter bot detection through unsupervised machine learning

    Jeremy Wu, Eric Teng, and Ziyue Cao. Twitter bot detection through unsupervised machine learning. In 2022 IEEE International Conference on Big Data (Big Data) , pages 5833–5839, 2022

    2022

  36. [44]

    An unsupervised method for social network spammer detection based on user information interests

    Darshika Koggalahewa, Yue Xu, and Ernest Foo. An unsupervised method for social network spammer detection based on user information interests. Journal of Big Data , 9(1):Article number: 7, January 2022

    2022

  37. [45]

    Daniele A. G. Lopes, Marcelo A. Marotta, Marcelo Ladeira, and Jo ˜ao J. C. Gondim. Botnet detection based on network flow analysis using inverse statistics. In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI) , pages 1–6, 2022

    2022

  38. [46]

    Detect me if you can: Spam bot detection using inductive representation learning

    Seyed Ali Alhosseini, Raad Bin Tareaf, Pejman Najafi, and Christoph Meinel. Detect me if you can: Spam bot detection using inductive representation learning. In Companion Proceedings of The 2019 World Wide Web Conference, WWW ’19, page 148–153, New York, NY , USA,

    2019

  39. [47]

    Heterogeneity- aware twitter bot detection with relational graph transformers

    Shangbin Feng, Zhaoxuan Tan, Rui Li, and Minnan Luo. Heterogeneity- aware twitter bot detection with relational graph transformers. In AAAI Conference on Artificial Intelligence , 2021

    2021

  40. [48]

    You talkin’ to me? exploring human/bot communication patterns during riot events

    Ema Ku ˇsen and Mark Strembeck. You talkin’ to me? exploring human/bot communication patterns during riot events. Information Processing & Management , 57(1):102126, 2020

    2020

  41. [49]

    Twitter bot detection using social network analysis

    Thi Bui and Katerina Potika. Twitter bot detection using social network analysis. In 2022 Fourth International Conference on Transdisciplinary AI (TransAI), pages 87–88, 2022

    2022

  42. [50]

    Detecting bots in social-networks using node and structural embeddings

    Ali Dehghan, Krzysztof Siuta, Andrzej Skorupka, Anuja Dubey, An- thony Betlen, Derek Miller, Wei Xu, Bogdan Kami ´nski, and Paweł Prałat. Detecting bots in social-networks using node and structural embeddings. Journal of Big Data , 10(1):119, 2023

    2023

  43. [51]

    Nguyen, Bay V o, and Unil Yun

    Phu Pham, Loan T.T. Nguyen, Bay V o, and Unil Yun. Bot2vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks. Information Systems, 103:101771, 2022

    2022

  44. [52]

    Opinion spam detection: Using multi-iterative graph-based model

    Shirin Noekhah, Naomie binti Salim, and Nor Hawaniah Zakaria. Opinion spam detection: Using multi-iterative graph-based model. In- formation Processing & Management , 57(1):102140, 2020

    2020

  45. [53]

    Hofa: Twitter bot detection with homophily- oriented augmentation and frequency adaptive attention

    Sen Ye, Zhaoxuan Tan, Zhenyu Lei, Ruijie He, Hongrui Wang, Qinghua Zheng, and Minnan Luo. Hofa: Twitter bot detection with homophily- oriented augmentation and frequency adaptive attention. arXiv preprint arXiv:2306.12870, 2023

    2023 arXiv

  46. [54]

    Similcatch: Enhanced social spammers detection on twitter using markov random fields

    Nour El-Mawass, Paul Honeine, and Laurent Vercouter. Similcatch: Enhanced social spammers detection on twitter using markov random fields. Information Processing & Management , 57(6):102317, 2020

    2020

  47. [55]

    Auto-gnn: Neural architecture search of graph neural networks

    Kaixiong Zhou, Xiao Huang, Qingquan Song, Rui Chen, and Xia Hu. Auto-gnn: Neural architecture search of graph neural networks. Frontiers in Big Data , 5, 2022

    2022

  48. [56]

    Graph neural architecture search

    Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. Graph neural architecture search. In Proceedings of the Twenty-Ninth Interna- tional Joint Conference on Artificial Intelligence , IJCAI’20, 2021

    2021

  49. [57]

    Simplifying architecture search for graph neural network

    Huan Zhao, Lanning Wei, and Quanming Yao. Simplifying architecture search for graph neural network. ArXiv, abs/2008.11652, 2020

    2008 arXiv

  50. [58]

    Matheus Nunes and Gisele L. Pappa. Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part I. Springer International Publishing, 2020

    2020

  51. [59]

    Meta-gnas: Meta-reinforcement learning for graph neural architecture search

    YuFei Li, Jia Wu, and TianJin Deng. Meta-gnas: Meta-reinforcement learning for graph neural architecture search. Engineering Applications of Artificial Intelligence , 123:106300, 2023

    2023

  52. [60]

    Learning graph convolutional network for skeleton-based human action recog- nition by neural searching

    Wei Peng, Xiaopeng Hong, Haoyu Chen, and Guoying Zhao. Learning graph convolutional network for skeleton-based human action recog- nition by neural searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03):2669–2676, 4 2020

    2020

  53. [61]

    Jiang and P

    S. Jiang and P. Balaprakash. Graph neural network architecture search for molecular property prediction. In 2020 IEEE International Confer- ence on Big Data (Big Data) , pages 1346–1353, Los Alamitos, CA, USA, dec 2020. IEEE Computer Society

    2020

  54. [62]

    Fast evolutionary neural architecture search by contrastive predictor with linear regions

    Yameng Peng, Andy Song, Vic Ciesielski, Haytham Fayek, and Xiaojun Chang. Fast evolutionary neural architecture search by contrastive predictor with linear regions. In Proceedings of the Genetic and Evolutionary Computation Conference , GECCO ’23, page 1257–1266, New York, NY ...

    2023

  55. [63]

    Evolutionary architecture search via adaptive parameter control and gene potential contribution

    Ronghua Shang, Songling Zhu, Hangcheng Liu, Teng Ma, Weitong Zhang, Jie Feng, Licheng Jiao, and Rustam Stolkin. Evolutionary architecture search via adaptive parameter control and gene potential contribution. Swarm and Evolutionary Computation , 82:101354, 2023

    2023

  56. [64]

    Alexandre

    Vasco Lopes, Miguel Santos, Bruno Degardin, and Lu ´ıs A. Alexandre. Guided evolutionary neural architecture search with efficient perfor- mance estimation. Neurocomputing, page 127509, 2024

    2024

  57. [65]

    Search to aggregate neighborhood for graph neural network

    Huan ZHAO, Quanming Y AO, and Weiwei TU. Search to aggregate neighborhood for graph neural network. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) , pages 552–563, 2021

    2021

  58. [66]

    One-shot graph neural architecture search with dynamic search space

    Yanxi Li, Zean Wen, Yunhe Wang, and Chang Xu. One-shot graph neural architecture search with dynamic search space. Proceedings of the AAAI Conference on Artificial Intelligence , 35(10):8510–8517, May 2021

    2021

  59. [67]

    A neural architecture search method based on gradient descent for remaining useful life estimation

    Jiakun Zhao, Ruifeng Zhang, Zheng Zhou, Si Chen, Ju Jin, and Qingfang Liu. A neural architecture search method based on gradient descent for remaining useful life estimation. Neurocomputing, 438:184–194, 2021

    2021

  60. [68]

    Online game bot detection based on party-play log analysis

    Ah Reum Kang, Jiyoung Woo, Juyong Park, and Huy Kang Kim. Online game bot detection based on party-play log analysis. Computers & Mathematics with Applications , 65(9):1384–1395, 2013. Advanced Information Security

    2013

  61. [69]

    A high level security mechanism for internet polls

    Shahriar Mohammadi and Hossein Abbasimehr. A high level security mechanism for internet polls. In 2010 2nd International Conference on Signal Processing Systems , volume 3, pages V3–101–V3–105, 2010

    2010

  62. [70]

    Twibot-22: towards graph- based twitter bot detection

    Shangbin Feng, Zhaoxuan Tan, Herun Wan, Ningnan Wang, Zilong Chen, Binchi Zhang, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, Xinshun Feng, Qingyue Zhang, Hongrui Wang, Yuhan Liu, Yuyang Bai, Heng Wang, Zijian Cai, Yanbo Wang, Lijing Zheng, Zihan Ma, Jundong Li, and ...

    2024

  63. [71]

    A multimodal approach for dementia detection from spontaneous speech with tensor fusion layer

    Loukas Ilias, Dimitris Askounis, and John Psarras. A multimodal approach for dementia detection from spontaneous speech with tensor fusion layer. In 2022 IEEE-EMBS International Conference on Biomed- ical and Health Informatics (BHI) , pages 1–5, 2022

    2022

  64. [72]

    Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech

    Loukas Ilias and Dimitris Askounis. Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech. Knowledge-Based Systems, 277:110834, 2023

    2023

  65. [73]

    Neural architecture search with multimodal fusion methods for diagnosing dementia

    Michail Chatzianastasis, Loukas Ilias, Dimitris Askounis, and Michalis Vazirgiannis. Neural architecture search with multimodal fusion methods for diagnosing dementia. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), page...

    2023

  66. [2019]

    Association for Computing Machinery. 14

  67. [2023]

    Association for Computing Machinery

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.