Stylometry and collaborative authorship: Eddy, Lovecraft, and ‘The Loved Dead’

Gladwin, Alexander A. G.; Lavin, Matthew J.; Look, Daniel M.

doi:10.1093/llc/fqv026

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Then the classification is performed by finding the hyper-plane that differentiate the classes very well by making the distance interval between each category maximize each other. As reported by [25], the calculation of minimum distance of hyper-plane is defined in 2:…”

Section: Support Vector Machinesmentioning

confidence: 99%

Author identification for Under-Resourced language (KadazanDusun)

Tarmizi

Saee

Ibrahim

2020

IJEECS

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<span>This paper presents the task of Author Identification for KadazanDusun language by using tweets as the source of data to perform Author Identification task of short text on KadazanDusun, which is considered as one the under-resourced language in Malaysia. The aim of this paper is to demonstrate Author Identification of short text on KadazanDusun. Besides, this paper also examines the performance of two machine learning algorithms on the KadazanDusun data set by analyzing the stylometric features. Stylometric features are used to quantify the writing styles of the authors which includes character n-grams and word n-grams. The workflow of Author Identification implements the machine learning approach to solve the single-labelled multi-class problem and predict the author of a given message in KadazanDusun. Two classifiers are used to compare the accuracy including Naïve Bayes and Support Vector Machine (SVM). The results show that the combination of n-grams which is word-level unigram and {1-5}-grams with character 3-grams are the most relevant stylometric features in identifying the author of KadazanDusun message with an accuracy of 80.17%. The results also show that SVM classifier has outperformed Naive Bayes in this Author Identification task with the accuracy of 80.17%.</span>

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Section: Support Vector Machinesmentioning

confidence: 99%

Author identification for Under-Resourced language (KadazanDusun)

Tarmizi

Saee

Ibrahim

2020

IJEECS

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“…Antonia, Craig and Elliott's conclusion that the optimal parameters and measures vary between corpora seems to be confirmed by studies such as Enrico Tuccinardi's, who demonstrates that character grams are more suitable in shorter documents (Tuccinardi 2016) and Lisa Pearl, Kristine Lu and Anousheh Haghighi's analysis of idiolect in epistolary literature, which allows for the weighting of some features as being more important than others (Pearl, Lu, Haghighi 2017). These findings culminate in the developing tendency towards the application of a diversity of methods applied to a similarly diverse set of parameters, for example discriminative words, word lengths, character-based frequency analysis, word-length, POS tags, measures for vocabulary richness, to which vector space representation PCA, hierarchical clustering, SVM, random forests, k-nearest neighbours, Delta or rolling Delta, the application of Delta to sequential windows of text, may be applied (Gladwin, Lavin, Look 2017;Hou, Jiang 2016;Saccenti, Tenori 2015;Sayoud 2012). Rybicki and Eder (2011) to generalise Delta's functionality by applying it to other languages, attaining high levels of success in French, German, Hungarian and Italian corpora but poorer results for Latin and Polish.…”

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confidence: 99%

1 | 2 | 2020 Fusions

2020

mag

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Certificazione scientifica delle Opere pubblicate da Edizioni Ca' Foscari -Digital Publishing: tutti i saggi pubblicati hanno ottenuto il parere favorevole da parte di valutatori esperti della materia, attraverso un processo di revisione anonima sotto la responsabilità del Comitato scientifico della rivista. La valutazione è stata condotta in aderenza ai criteri scientifici ed editoriali di Edizioni Ca' Foscari. Scientific certification of the works published by Edizioni Ca' Foscari -Digital Publishing: all essays published in this volume have received a favourable opinion by subject-matter experts, through an anonymous peer review process under the responsibility of the Scientific Committee of the journal.

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Who could be behind QAnon? Authorship attribution with supervised machine-learning

Cafiero,

Camps

2023

Digital Scholarship in the Humanities

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A series of social media posts on 4chan then 8chan, signed under the pseudonym ‘Q’, started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of the texts written by Q and to those written by a list of suspects provided by journalistic investigation. To identify the authors of these posts, serious challenges have to be addressed. The ‘Q drops’ are very short texts, written in a way that constitute a sort of literary genre in itself, with very peculiar features of style. These texts might have been written by different authors, whose other writings are often hard to find. After an online ethnography of the movement, necessary to collect enough material written by these thirteen potential authors, we use supervised machine learning to build stylistic profiles for each of them. We then performed a ‘rolling analysis’, looking repeatedly through a moving window for parts of Q’s writings matching our profiles. We conclude that two different individuals, Paul F. and Ron W., are the closest match to Q’s linguistic signature, and they could have successively written Q’s texts. These potential authors are not high-ranked personality from the US administration, but rather social media activists.

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Stylometry and collaborative authorship: Eddy, Lovecraft, and ‘The Loved Dead’

Cited by 8 publications

References 12 publications

Author identification for Under-Resourced language (KadazanDusun)

Author identification for Under-Resourced language (KadazanDusun)

1 | 2 | 2020 Fusions

Who could be behind QAnon? Authorship attribution with supervised machine-learning

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