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REVIEW 3 major objections 5 minor 59 references

The Limitations of Stylometry for Detecting Machine-Generated Fake News

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Stylometry cannot tell whether AI-generated news is true or false, because language models write true and false content in the same style.

desk verdict A genuine, useful negative result about veracity detection for machine-generated text, though the broad 'stylometry fails' claim outruns the evidence of a single neural detector. read the letter →

arxiv 1908.09805 v2 pith:QEVKJ7KD submitted 2019-08-26 cs.CL cs.CY

Tal Schuster , Roei Schuster , Darsh J Shah , Regina Barzilay This is my paper · ORCID
classification cs.CLcs.CY
keywords stylometrymachine-generatedfakenewslanguagemodelsprovenancedetectionveracitybenchmarksneuralmisinformation
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 argues that stylometry, the analysis of writing style, cannot reliably distinguish false machine-generated news from true machine-generated news, even though it can tell machine text from human text. Using a single state-of-the-art style-based detector on two new benchmarks—an auto-completion task and an article-editing task—the authors show that the detector identifies machine authorship with up to 95 percent accuracy on a single generated sentence, but its accuracy for telling true from false machine text peaks at 71 percent and falls to 53 percent when negations are inserted. The reason is that language models produce stylistically consistent text regardless of whether the content is truthful, unlike human writers who show style changes when deceiving. The paper therefore recommends that defenses against machine-generated misinformation move beyond style and incorporate fact-checking or other external evidence.

What carries the argument

The load-bearing object is the Grover-Mega discriminator, a large Transformer-based neural classifier that is fine-tuned to decide whether a text was written by a language model or a human. The paper's controlled comparison is what isolates style from truth: in the QA-extension benchmark, the same Grover-Mega model generates both the true and the false answers, and in the article-modification benchmark a second model (GPT-2) decides where to add or remove negations while the underlying article's style is preserved. This pairing ensures that any observable stylistic difference between the classes would have to reflect the veracity of the content, and the benchmark results show that no usable difference exists. The two datasets are therefore the experimental instruments that carry the argument.

What would settle it

A style-only classifier that, after fine-tuning on the paper's two veracity benchmarks (QA-extension and article-modification), exceeds roughly 85 percent balanced accuracy in distinguishing true from false machine text would falsify the claim that LM-generated true and false content are stylistically indistinguishable.

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Extended reading notes

Core claim

The central discovery is the decoupling of provenance from veracity in machine-written text. On the provenance task, a fine-tuned Grover-Mega discriminator classifies full machine versus human articles with 94 percent accuracy and detects even a single machine-written sentence within a human article at 95 percent accuracy. On the veracity task, where both 'real' and 'fake' examples are generated by the same language model, the same detector reaches only 71 percent accuracy on the question-answering extension benchmark and 53 to 65 percent on the article-modification benchmark; a simple sentence-length baseline already accounts for part of the QA-extension score. These numbers support the paper's claim that LM-generated true and false content are stylistically indistinguishable, and that stylometry can prevent impersonation but not detect LM-generated misinformation.

Load-bearing premise

The paper's sweeping conclusion assumes the Grover-Mega discriminator is representative of all stylometry detectors; if another style-based method could tell true from false machine text, the claim that stylometry fails would not generalize.

Editorial extensions

If this is right

  • Detectors can still flag text as machine-generated, which is useful against impersonation, but should not be used to infer that the text is false.
  • Fake-news evaluation benchmarks must include mixed human-machine and fully machine text with both true and false content, or they will overstate detection performance.
  • Fact-checking and methods that use external evidence become the central defense against machine-generated misinformation.
  • Human readers, when given external sources, detect machine-generated false claims much better than text-only classifiers, supporting the development of human-in-the-loop tools.

Reading between the lines

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

  • The paper's argument implies that any detector restricted to the text itself will face the same barrier whenever true and false content are generated by the same model, so future style-based defenses would need to find signals the generation process does not control.
  • A natural testable extension is to reuse the two benchmarks to evaluate whether constraining generation with a fact-checking module (for example, decoding that prefers claims supported by an external corpus) makes the generated true and false texts diverge stylistically enough to be separated.
  • Extending the same experimental design to non-English news, tables, or medical text would show whether the style-veracity decoupling is a general property of current language models or specific to English prose.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper asks whether stylometry can distinguish true from false machine-generated text when both are produced by the same language model. The authors construct two benchmarks: a QA-extension task in which Grover generates answers to news questions and humans label them as true or false, and an article-modification task in which negations are added/removed by GPT-2 Medium. Using a Grover-Mega discriminator fine-tuned adaptively on each attack, they report accuracies of 71%, 53%, 65%, and 65% on the veracity tasks, versus 90–95% on provenance detection tasks (full articles and vanilla extensions). They conclude that stylometry can detect machine-generated text (provenance) but cannot identify intentionally misleading content, and they recommend non-stylometric approaches such as fact checking. Human evaluations and an analysis of the length confound in the QA task are included.

Significance. The paper makes a valuable negative claim with practical implications. The internal control—showing that the same detector achieves high provenance accuracy while failing on veracity—is a strong and well-executed comparison. The benchmarks (QA extension and modification) are reusable resources, the authors release the data, and the human evaluation anchors the machine results. The length-confound analysis in Section 4.1 is honest and useful. If the generalization is properly scoped, the result will be influential for the fake-news detection community. The main weakness is that the universal negative about 'stylometry' is supported by only one neural detector; the near-chance results also lack confidence intervals.

major comments (3)
  1. [Section 3 and Section 6] The conclusion generalizes from one detector to the entire class of stylometry methods. Section 3 states that 'We used a Grover-Mega discriminator for all of the experiments,' yet Section 6 concludes that 'stylometry-based classifiers cannot identify auto-generated intentionally misleading content,' and the abstract claims 'stylometry is limited.' Grover-Mega is a single neural model (a Transformer), and the paper surveys but does not evaluate feature-based stylometry methods (function words, POS tags, syntactic structure) in Section 2. Without experiments or an argument showing that Grover-Mega is representative of the class, the blanket conclusion exceeds the evidence. Please either narrow the claims to the tested model (e.g., 'the Grover-Mega neural stylometry detector') or add evaluations of at least one or two feature-based stylometric baselines on the same benchmarks.
  2. [Table 1 and Section 4.1] The key veracity accuracies are reported as point estimates without confidence intervals or significance tests. In particular, the 53% accuracy for m=2 modification may be statistically indistinguishable from chance; the statement in Section 4.1 that 'the classifier fails completely' needs support from a confidence interval or a binomial test. Similarly, the 62% accuracy on the short-answer QA subset and the 65% accuracies for m=6 and m=10 lack error bars. Please add bootstrap confidence intervals (or exact binomial intervals) for the accuracy/F1 values in Table 1, and ideally for the provenance results in Table 2 for comparison.
  3. [Section 4.1 and Table 1] The length confound in the QA-extension task is acknowledged but not controlled for at the level required by the central claim. The authors show that a length-only linear classifier reaches 56% and that the detector's accuracy falls from 71% to 62% when restricted to short false answers, but this post-hoc subset analysis is not accompanied by a length-matched evaluation or a significance test. Since the near-chance performance on the veracity task is a key evidence for the paper's conclusion, please provide a direct comparison on a length-matched test set (or report accuracy conditioned on answer length) to demonstrate that the remaining signal is not an artifact of the dataset construction.
minor comments (5)
  1. [Section 3] The manuscript describes Grover-Mega as a 'stylometry detector' without defining why a Transformer-based neural classifier counts as stylometry; please clarify the connection (e.g., it operates on surface form and is trained for discrimination) in one sentence.
  2. [Section 4] The human evaluation details are thin: exact participant counts, recruitment, and instructions are not given for the 'about 100 examples' per dataset, and the reported Cohen's kappa applies to the veracity labeling of generated answers rather than to the human detection task. Please add a sentence specifying the evaluation protocol.
  3. [Section 5, Table 2] The text in Section 5 mentions the 'stronger Grover-Mega generator' for the QA setting, but the reader is not told whether this generator is the same checkpoint as the discriminator; please state model names and parameters clearly once in Section 3 and refer back.
  4. [References] Some references appear incomplete (e.g., 'Doc' and 'Hox' entries lack authors and years), and a few URLs are given without access dates; please complete the bibliography according to the journal style.
  5. [Abstract] The term 'real' is used for both human-written text and LM-generated truthful text; since the paper's distinction is veracity rather than provenance, consider a terminology note to avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical finding from an external veracity benchmark, not a derivation from fitted inputs.

full rationale

This paper is an empirical evaluation, not a derivation. The 'real' and 'fake' labels come from human veracity judgments and external news sources, not from the detector's own outputs, so the low veracity accuracy (53-71%) is not an artifact of definitional construction. The provenance control in Section 5 (94% accuracy on the same detector) shows the failure is specific to the veracity task rather than detector capacity, and the paper's own length-baseline analysis in Section 4.1 checks a potential confound. The only self-citation (Schuster et al. 2019) is used as background support for the claim that fact-verification models underperform humans, which is not load-bearing for the stylometry conclusion. The broader inference from Grover-Mega to 'stylometry-based classifiers cannot' rests on treating one neural discriminator as representative of the whole approach class; that is an external-validity or evidential concern, not a circularity, because no step in the paper reduces by construction to its own inputs. No fitted parameter is renamed as a prediction, and no load-bearing uniqueness theorem or ansatz is imported from the authors' prior work.

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

No free parameters or invented entities. The central claim rests on benchmark construction choices and the assumption that Grover-Mega represents stylometry, listed in axioms.

assumptions (4)
  • domain assumption Grover-Mega is the state-of-the-art stylometry-based classifier and is representative of stylometry approaches.
    The paper uses only Grover-Mega as the detector (Section 3) and generalizes its failure to stylometry as a whole (Section 6).
  • domain assumption News articles from The New York Times and CNN are assumed truthful and serve as ground-truth real text.
    Section 3 explicitly assumes reputable news articles like The New York Times are real.
  • domain assumption Human annotation of generated answers reliably separates true from false content.
    Section 4 reports inter-annotator kappa of 0.78 and filters 29% of answers as nonsensical; the benchmark labels depend on this.
  • domain assumption Local negation edits preserve the article's overall style distribution.
    The modification attack in Section 4 relies on adding and removing negations to create semantic inversion without anomaly in negation counts.

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Pith. "Pith review of The Limitations of Stylometry for Detecting Machine-Generated Fake News." pith.science (2026). https://pith.science/paper/QEVKJ7KD

@misc{pith2026190809805,
  author       = {Pith},
  title        = {Pith review of: The Limitations of Stylometry for Detecting Machine-Generated Fake News},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QEVKJ7KD}},
  note         = {Machine review of arXiv:1908.09805}
}
read the original abstract

Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading misinformation. In light of these concerns, several studies have proposed to detect machine-generated fake news by capturing their stylistic differences from human-written text. These approaches, broadly termed stylometry, have found success in source attribution and misinformation detection in human-written texts. However, in this work, we show that stylometry is limited against machine-generated misinformation. While humans speak differently when trying to deceive, LMs generate stylistically consistent text, regardless of underlying motive. Thus, though stylometry can successfully prevent impersonation by identifying text provenance, it fails to distinguish legitimate LM applications from those that introduce false information. We create two benchmarks demonstrating the stylistic similarity between malicious and legitimate uses of LMs, employed in auto-completion and editing-assistance settings. Our findings highlight the need for non-stylometry approaches in detecting machine-generated misinformation, and open up the discussion on the desired evaluation benchmarks.

Figures

Figures reproduced from arXiv: 1908.09805 by the authors.

Figure 1
Figure 1. Examples of the fake class in our experiments. (a) In the news question answering (Section 4), a CNN article is presented with two examples of questions (bold) from newsQA (Trischler et al. 2017) and Grover’s generated answer (red). The first answer is verified by a human annotator to be false and the second as true. (b) In article modification (m = 6) (Section 4), the negations are marked with a cross-line for dele… view at source ↗

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Works this paper leans on

59 extracted references · 54 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sentence := #2 '...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    doc product medical q&a with deep language models

    Doc. doc product medical q&a with deep language models. https://github.com/re-search/DocProduct

    work page

  4. [4]

    list of hoaxes on wikipedia

    Hox. list of hoaxes on wikipedia. https://en.wikipedia.org/wiki/Wikipedia:List_of_hoaxes_on_Wikipedia

    work page

  5. [5]

    Abramov, Polina Shafran and Roman V Yampolskiy. 2019. Automatic iq estimation using stylometric methods. In Handbook of Research on Learning in the Age of Transhumanism. IGI Global, pages 32--45

    work page 2019

  6. [6]

    Afroz, Sadia, Michael Brennan, and Rachel Greenstadt. 2012. Detecting hoaxes, frauds, and deception in writing style online. 2012 IEEE Symposium on Security and Privacy, pages 461--475

    work page 2012

  7. [7]

    Afroz, Sadia, Aylin Caliskan, Ariel Stolerman, Rachel Greenstadt, and Damon McCoy. 2014. Doppelg \"a nger finder: Taking stylometry to the underground. 2014 IEEE Symposium on Security and Privacy, pages 212--226

    work page 2014

  8. [8]

    Augenstein, Isabelle, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019. M ulti FC : A real-world multi-domain dataset for evidence-based fact checking of claims. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Confere...

    work page 2019

Show all 59 references
  1. [9]

    Bakhtin, Anton, Sam Gross, Myle Ott, Yuntian Deng, Marc'Aurelio Ranzato, and Arthur Szlam. 2019. Real or fake? learning to discriminate machine from human generated text. arXiv preprint arXiv:1906.03351

    2019 arXiv

  2. [10]

    Benjakob, Omer. 2019. The fake nazi death camp: Wikipedia's longest hoax, exposed. https://tinyurl.com/y3m2wd9k. Haaretz

    2019

  3. [11]

    and Adrienne Y

    Bond, Gary D. and Adrienne Y. Lee. 2005. Language of lies in prison: linguistic classification of prisoners' truthful and deceptive natural language. Applied Cognitive Psychology, 19(3):313--329

    2005

  4. [12]

    Brennan, Michael, Sadia Afroz, and Rachel Greenstadt. 2012. Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity. ACM Transactions on Information and System Security (TISSEC), 15(3):12

    2012

  5. [13]

    Caliskan-Islam, Aylin, Richard Harang, Andrew Liu, Arvind Narayanan, Clare Voss, Fabian Yamaguchi, and Rachel Greenstadt. 2015. De-anonymizing programmers via code stylometry. In 24th USENIX Security Symposium ( USENIX Security 15) , pages 255--270, USENIX Association, Washington, D.C

    2015

  6. [14]

    Castillo, Carlos, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on twitter. In Proceedings of the 20th International Conference on World Wide Web, pages 67 -- 684, Association for Computing Machinery, New York, NY, USA

    2011

  7. [15]

    Choshen, Leshem, Dan Eldad, Daniel Hershcovich, Elior Sulem, and Omri Abend. 2019. The language of legal and illegal activity on the D arknet. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4271--4279, Association for Computat...

    2019

  8. [16]

    von Davier, Matthias. 2019. Training optimus prime, md: Generating medical certification items by fine-tuning openai's gpt2 transformer model. arXiv preprint arXiv:1908.08594

    2019 arXiv

  9. [17]

    Dostoyevsky, Fyodor. 1866. Crime and Punishment. The Russian Messenger

  10. [18]

    Enos, Frank, Elizabeth Shriberg, Martin Graciarena, Julia Hirschberg, and Andreas Stolcke. 2007. Detecting deception using critical segments. In International Speech Communication Association, pages 2281--2284

    2007

  11. [19]

    Feng, Song, Ritwik Banerjee, and Yejin Choi. 2012. Syntactic stylometry for deception detection. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2, ACL '12, pages 171--175, Association for Computational Linguist...

    2012

  12. [20]

    Frank, Mark G, Melissa A Menasco, and Maureen O'Sullivan. 2008. Human behavior and deception detection. Wiley handbook of science and technology for homeland security, pages 1--13

    2008

  13. [21]

    Gehrmann, Sebastian, Hendrik Strobelt, and Alexander Rush. 2019. GLTR : Statistical detection and visualization of generated text. In Proceedings of the 57th Conference of the Association for Computational Linguistics: System Demonstrations, pages 111--116, Association for Com...

    2019

  14. [22]

    Goswami, Sumit, Sudeshna Sarkar, and Mayur Rustagi. 2009. Stylometric analysis of bloggers' age and gender. In International AAAI Conference on Web and Social Media

    2009

  15. [23]

    Gupta, Aditi, Ponnurangam Kumaraguru, Carlos Castillo, and Patrick Meier. 2014. Tweetcred: Real-time credibility assessment of content on twitter. In International Conference on Social Informatics, pages 228--243, Springer

    2014

  16. [24]

    Hanselowski, Andreas, PVS Avinesh, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M Meyer, and Iryna Gurevych. 2018. A retrospective analysis of the fake news challenge stance-detection task. In Proceedings of the 27th International Conference on Computatio...

    2018

  17. [25]

    House, Patrick. 2019. I, language robot. https://lareviewofbooks.org/article/i-language-robot/. Los Angeles Review of Books

    2019

  18. [26]

    Kochkina, Elena, Maria Liakata, and Arkaitz Zubiaga. 2018. All-in-one: Multi-task learning for rumour verification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3402--3413, Association for Computational Linguistics, Santa Fe, New Mexico, USA

    2018

  19. [27]

    Liu, Yang and Yi-Fang Wu. 2018. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In AAAI Conference on Artificial Intelligence

    2018

  20. [28]

    Matsumoto, David, Hyi Sung Hwang, Lisa Skinner, and Mark Frank. 2011. Evaluating truthfulness and detecting deception. FBI L. Enforcement Bull., 80:1

    2011

  21. [29]

    Mazar, Nina, On Amir, and Dan Ariely. 2008. The dishonesty of honest people: A theory of self-concept maintenance. Journal of marketing research, 45(6):633--644

    2008

  22. [30]

    Mihalcea, Rada and Carlo Strapparava. 2009. The lie detector: Explorations in the automatic recognition of deceptive language. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 309 -- 312, Association for Computational Linguistics, USA

    2009

  23. [31]

    Mosteller, Frederick and David L. Wallace. 1963. Inference in an authorship problem. Journal of the American Statistical Association, 58(302):275--309

    1963

  24. [32]

    Mitchell

    Nakashole, Ndapandula and Tom M. Mitchell. 2014. Language-aware truth assessment of fact candidates. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1009--1019, Association for Computational Linguistics,...

    2014

  25. [33]

    Neal, Tempestt J., Kalaivani Sundararajan, Aneez Fatima, Yiming Yan, Yingfei Xiang, and Damon L. Woodard. 2017. Surveying stylometry techniques and applications. ACM Comput. Surv., 50:86:1--86:36

    2017

  26. [34]

    Ott, Myle, Yejin Choi, Claire Cardie, and Jeffrey T. Hancock. 2011. Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 309--319, Associ...

    2011

  27. [35]

    P \'e rez-Rosas, Ver \'o nica, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2018. Automatic detection of fake news. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3391--3401

    2018

  28. [36]

    Pisarevskaya, Dina. 2017. Deception detection in news reports in the R ussian language: Lexics and discourse. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism , pages 74--79, Association for Computational Linguistics, Copenhagen, Denmark

    2017

  29. [37]

    Popat, Kashyap, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018. D e C lar E : Debunking fake news and false claims using evidence-aware deep learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 22--32, Associa...

    2018

  30. [38]

    Potthast, Martin, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2018. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 231--24...

    2018

  31. [39]

    Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog, 1(8)

    2019

  32. [40]

    Rashkin, Hannah, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2931--2937

    2017

  33. [41]

    Rudanko, Juhani. 1982. Towards a description of negatively conditioned subject operator inversion in english. English Studies, 63(4):348--359

    1982

  34. [42]

    Sari, Yunita, Andreas Vlachos, and Mark Stevenson. 2017. Continuous n-gram representations for authorship attribution. In Proceedings of the 15th Conference of the E uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers , pages 267--273, Asso...

    2017

  35. [43]

    Schuster, Tal, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, and Regina Barzilay. 2019. Towards debiasing fact verification models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Internatio...

    2019

  36. [44]

    Shu, Kai, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1):22--36

    2017

  37. [45]

    Thompson, Andrew. 2017. All the news dataset. https://www.kaggle.com/snapcrack/all-the-news. Kaggle

    2017

  38. [46]

    Thorne, James, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2019. Evaluating adversarial attacks against multiple fact verification systems. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International J...

    2019

  39. [47]

    Thorne, James, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2018. The fact extraction and verification (fever) shared task. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 1--9

    2018

  40. [48]

    Trischler, Adam, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017. N ews QA : A machine comprehension dataset. In Proceedings of the 2nd Workshop on Representation Learning for NLP , pages 191--200, Association for Computation...

    2017

  41. [49]

    Tschiatschek, Sebastian, Adish Singla, Manuel Gomez Rodriguez, Arpit Merchant, and Andreas Krause. 2018. Fake news detection in social networks via crowd signals. In Companion Proceedings of the The Web Conference 2018, pages 517--524, International World Wide Web Conferences ...

    2018

  42. [50]

    Tweedie, Fiona J., Shreyan Singh, and David I. Holmes. 1996. Neural network applications in stylometry: The federalist papers. Computers and the Humanities, 30:1--10

    1996

  43. [51]

    Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998--6008

    2017

  44. [52]

    Volkova, Svitlana, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. 2017. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume ...

    2017

  45. [53]

    Vosoughi, Soroush, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science, 359(6380):1146--1151

    2018

  46. [54]

    Wang, William Yang. 2017. `` liar, liar pants on fire '' : A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 422--426, Association for Computational Lingui...

    2017

  47. [55]

    Wardle, Claire and Hossein Derakhshan. 2017. Information disorder: Toward an interdisciplinary framework for research and policy making

    2017

  48. [56]

    Wolf, Thomas, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R'emi Louf, Morgan Funtowicz, and Jamie Brew. 2019. Huggingface's transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771

    2019 arXiv

  49. [57]

    Zellers, Rowan, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. In Advances in Neural Information Processing Systems 32, pages 9054--9065, Curran Associates, Inc

    2019

  50. [58]

    Zhao, Zhe, Paul Resnick, and Qiaozhu Mei. 2015. Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th International Conference on World Wide Web, pages 1395--1405, International World Wide Web Conferences Steering Committee

    2015

  51. [59]

    Zhou, Xinyi and Reza Zafarani. 2018. Fake news: A survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315

    2018 arXiv

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