Computational authorship verification method attributes a new work to a major 2nd century<scp>A</scp>frican author

Stover, Justin; Winter, Yaron; Koppel, Moshe; Kestemont, Mike

doi:10.1002/asi.23460

Cited by 30 publications

(16 citation statements)

References 15 publications

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“…Standard applications of stylometry include dating literary works and resolving questions of attribution (26)(27)(28)(29)(30). Both ad hoc stylometric analysis and supervised machine learning with stylometric features have proven successful for such applications (31)(32)(33), including for cases in Latin literature (34).…”

Section: Significancementioning

confidence: 99%

Quantitative criticism of literary relationships

Dexter

Katz

Tripuraneni

et al. 2017

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

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Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term "quantitative criticism," focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca's main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions.authorship attribution | cultural evolution | intertextuality | machine learning | stylometry T he study of literature relies on mapping interactions between texts. Ancient Greek critics understood the tragedies of Aeschylus in part through their relation to Homeric epic, and ancient Roman commentators interpreted words and phrases in texts by citing parallels in other works. Much of literary criticism today rests on understanding these vast networks of intertextuality, which often have profound consequences for the meaning of both individual texts and larger groupings by genre or period (1). Through quantitative analysis of formal elements and their change over time, the study of intertextuality can shed light on the cultural evolution of literature (2).A central challenge in the study of intertextuality is its heterogeneous nature. Literary parallels differ widely in both similarity and scope (Fig. 1A). The relationship between the associated texts can range from obvious (direct quotation) to extremely subtle (artfully constructed indirect references, often referred to as allusions in literary study). Furthermore, parallels can operate on the level of individual words or phrases, short passages, or entire works and can involve verbal, syntactic, phonetic, or metrical features. As illustrated in Fig. 1A, intertexts can be of comparable similarity but very different scope; an adaptation of an entire work, for instance, can be thought of as a collection of many (local) allusions.In this paper, we focus on the quantitative characterization of intertextual rela...

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Section: Significancementioning

confidence: 99%

Quantitative criticism of literary relationships

Dexter

Katz

Tripuraneni

et al. 2017

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

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“…In this paper, we illustrate the broader applicability of computational authorship verification by reporting a high-profile case study from Classical Antiquity (Koppel & Seidman, 2013;Stover et al, 2016). The 'War Commentaries' by Julius Caesar (Corpus Caesarianum) refers to a group of Latin 20 prose commentaries, describing the military campaigns of the world-renowned statesman Julius Caesar (100-44 BC), the founder of the Roman Empire.…”

Section: Currently Receives Increasing Attention Inmentioning

confidence: 99%

Authenticating the writings of Julius Caesar

Kestemont

Stover

Koppel

et al. 2016

Expert Systems with Applications

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In this paper, we shed new light on the authenticity of the Corpus Caesarianum, a group of five commentaries describing the campaigns of Julius Caesar (100-44 BC), the founder of the Roman empire. While Caesar himself has authored at least part of these commentaries, the authorship of the rest of the texts remains a puzzle that has persisted for nineteen centuries. In particular, the role of Caesar's general Aulus Hirtius, who has claimed a role in shaping the corpus, has remained in contention. Determining the authorship of documents is an increasingly important authentication problem in information and computer science, with valuable applications, ranging from the domain of art history to counter-terrorism research.We describe two state-of-the-art authorship verification systems and benchmark them on 6 present-day evaluation corpora, as well as a Latin benchmark dataset. Regarding Caesar's writings, our analysis allow us to establish that Hirtius's claims to part of the corpus must be considered legitimate. We thus demonstrate how computational methods constitute a valuable methodological complement to traditional, expert-based approaches to document authentication.

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“…In the relevant literature, there are several examples of applications associated with author verification technology. In digital humanities, author verification methods have been used to reveal the authorship of disputed documents of great literary or historical importance (Kestemont, Stover, Koppel, Karsdorp, & Daelemans, 2016;Stover, Winter, Koppel, & Kestemont, 2016;Tuccinardi, 2017). In cybersecurity, it has been used to detect compromised accounts in social networks (Barbon, Igawa, & Bogaz Zarpelão, 2017), perform spearphishing filtering (Duman, Kalkan-Cakmakci, Egele, Robertson, & Kirda, 2016), and continuous authentication of users (Brocardo, Traore, Woungang, & Obaidat, 2017).…”

Section: Introductionmentioning

confidence: 99%

Improving author verification based on topic modeling

Potha

Stamatatos

2019

Asso for Info Science & Tech

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Authorship analysis attempts to reveal information about authors of digital documents enabling applications in digital humanities, text forensics, and cyber‐security. Author verification is a fundamental task where, given a set of texts written by a certain author, we should decide whether another text is also by that author. In this article we systematically study the usefulness of topic modeling in author verification. We examine several author verification methods that cover the main paradigms, namely, intrinsic (attempt to solve a one‐class classification task) and extrinsic (attempt to solve a binary classification task) methods as well as profile‐based (all documents of known authorship are treated cumulatively) and instance‐based (each document of known authorship is treated separately) approaches combined with well‐known topic modeling methods such as Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). We use benchmark data sets and demonstrate that LDA is better combined with extrinsic methods, while the most effective intrinsic method is based on LSI. Moreover, topic modeling seems to be particularly effective for profile‐based approaches and the performance is enhanced when latent topics are extracted by an enriched set of documents. The comparison to state‐of‐the‐art methods demonstrates the great potential of the approaches presented in this study. It is also demonstrates that even when genre‐agnostic external documents are used, the proposed extrinsic models are very competitive.

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Computational authorship verification method attributes a new work to a major 2nd centuryAfrican author

Cited by 30 publications

References 15 publications

Quantitative criticism of literary relationships

Quantitative criticism of literary relationships

Authenticating the writings of Julius Caesar

Improving author verification based on topic modeling

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