Duplicate and fake publications in the scientific literature: how many SCIgen papers in computer science?

Labbé, Cyril; Labbé, Dominique

doi:10.1007/s11192-012-0781-y

Cited by 93 publications

(77 citation statements)

References 15 publications

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“…The title of one paper generated by the software was: "Simulating Flip-flop Gates Using Peer-to-peer Methodologies." At least 120 such computer-generated papers were published in peer-reviewed scientific journals (Labbé & Labbé, 2013;Lott, 2014). Juster (1972, p. 23) states, "Few people would accept the naïve nochange model even if it were clearly shown to be more accurate."…”

Section: Why Simplicity Repels and Complexity Luresmentioning

confidence: 99%

Simple versus complex forecasting: The evidence

Green

Armstrong

2015

Journal of Business Research

174

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This article introduces this JBR Special Issue on simple versus complex methods in forecasting. Simplicity in forecasting requires that (1) method, (2) representation of cumulative knowledge, (3) relationships in models, and (4) relationships among models, forecasts, and decisions are all sufficiently uncomplicated as to be easily understood by decision-makers. Our review of studies comparing simple and complex methods -including those in this special issue -found 97 comparisons in 32 papers. None of the papers provide a balance of evidence that complexity improves forecast accuracy. Complexity increases forecast error by 27 percent on average in the 25 papers with quantitative comparisons. The finding is consistent with prior research to identify valid forecasting methods: all 22 previously identified evidence-based forecasting procedures are simple. Nevertheless, complexity remains popular among researchers, forecasters, and clients. Some evidence suggests that the popularity of complexity may be due to incentives: (1) researchers are rewarded for publishing in highly ranked journals, which favor complexity; (2) forecasters can use complex methods to provide forecasts that support decisionmakers' plans; and (3) forecasters' clients may be reassured by incomprehensibility. Clients who prefer accuracy should accept forecasts only from simple evidence-based procedures. They can rate the simplicity of forecasters' procedures using the questionnaire at simple-forecasting.com.

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Section: Why Simplicity Repels and Complexity Luresmentioning

confidence: 99%

Simple versus complex forecasting: The evidence

Green

Armstrong

2015

Journal of Business Research

174

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“…Many other machine-generated texts support for malicious purposes such as paper generation and fake review. Labbé and Labbé (2013) prove that artificial papers are produced by using abundant duplicated words and phrases. Therefore, they suggested an inter-textual distance to estimate the similarity between two word distributions and used the distance to recognize the machinegenerated text.…”

Section: Other Machine-generated Text Detectionmentioning

confidence: 94%

“…Other researchers prove that the coherence of the humanwritten text is better than the machine-translated one (Nguyen-Son et al, 2018, 2019. Beyond detecting machine-translated text, artificial fake reviews and papers are also recognized by readability (Juuti et al, 2018) and duplicate patterns (Labbé and Labbé, 2013), respectively. The limitation in all existing methods above is that they only analyze the intrinsic contents of machinegenerated texts but ignore the original processes which are used to produce the texts.…”

Section: Introductionmentioning

confidence: 99%

Detecting Machine-Translated Text using Back Translation

Nguyen-Son¹,

Phuong²,

Hidano³

et al. 2019

Proceedings of the 12th International Conference on Natural Language Generation

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Machine-translated text plays a crucial role in the communication of people using different languages. However, adversaries can use such text for malicious purposes such as plagiarism and fake review. The existing methods detected a machine-translated text only using the text's intrinsic content, but they are unsuitable for classifying the machine-translated and human-written texts with the same meanings. We have proposed a method to extract features used to distinguish machine/human text based on the similarity between the intrinsic text and its back-translation. The evaluation of detecting translated sentences with French shows that our method achieves 75.0% of both accuracy and F -score. It outperforms the existing methods whose the best accuracy is 62.8% and the F -score is 62.7%. The proposed method even detects more efficiently the back-translated text with 83.4% of accuracy, which is higher than 66.7% of the best previous accuracy. We also achieve similar results not only with F -score but also with similar experiments related to Japanese. Moreover, we prove that our detector can recognize both machine-translated and machineback-translated texts without the language information which is used to generate these machine texts. It demonstrates the persistence of our method in various applications in both low-and rich-resource languages.

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“…Here is a sentence from one abstract (since removed from IEEE Xplore): "Furthermore, it explored a pervasive tool for enabling pasteurization, which is used to show that context-free grammar and B-trees are largely compatible." Chatterbot output can now be detected automatically (12) and publishers find themselves, regrettably, forced to use such software, as well as anti-copying utilities.…”

Section: Consequencesmentioning

confidence: 99%

How many scientific papers are not original?

Lesk

2014

Proc. Natl. Acad. Sci. U.S.A.

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Duplicate and fake publications in the scientific literature: how many SCIgen papers in computer science?

Cited by 93 publications

References 15 publications

Simple versus complex forecasting: The evidence

Simple versus complex forecasting: The evidence

Detecting Machine-Translated Text using Back Translation

How many scientific papers are not original?

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