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\"How much of ourselves do we disclose when we speak or write? A person's accent may reveal, for example, whether they hail from Australia, or Ireland, or Mississippi. But it's not just where we were born-we divulge all sorts of information about ourselves and our identity through language. Level of education, gender, age, and even aspects of our personality can all be reliably determined by our vocabulary and grammar. To those who know what to look for, we give ourselves away every time we open our mouths or tap on a keyboard. But how unique is a person's linguistic identity? Can language be used to identify a specific person? To identify-or to exonerate-a murder suspect? To determine who authored a particular book? The answer to all these questions is yes. Forensic and computational linguists have developed methods that allow linguistic fingerprinting to be used in law enforcement. Similar techniques are used by literary scholars to identify the authors of anonymous or contested works of literature. Many people have heard that linguistic analysis helped to catch the Unabomber, or to unmask an anonymous editorialist-but how is it done? Linguistic Fingerprints will explain how these methods were developed and how they are used to solve forensic and literary mysteries. But these techniques aren't perfect, and the book will also include some cautionary tales about mistaken linguistic identity\"-- Provided by publisher.

As human-AI collaboration becomes increasingly prevalent in educational contexts, understanding and measuring the extent and nature of such interactions pose significant challenges. This research investigates authorship verification (AV) techniques to quantify AI assistance in academic writing, focusing on transparency and interpretability. We structured our investigation into three stages: dataset selection and expansion, AV method development, and systematic evaluation. Using three datasets in Stage 1, including PAN-14 and two from University of Melbourne students, we expanded the data to include LLM-generated texts, totalling 1,889 documents and 540 authorship problems from 506 students. Next, we developed an adapted Feature Vector Difference (FVD) authorship verification method to construct academic writing profiles for students, capturing meaningful stylistic features. Lastly, our AV method was evaluated across multiple scenarios including distinguishing between student-authored and LLM-generated texts, and detecting AI mimicry using standard authorship verification metrics such as AUC, c@1, and F1. Results showed that our approach effectively distinguished between student-authored and AI-generated texts, even under mimicry scenarios, offering educators actionable insights into students' writing progress.

This paper presents a comprehensive study of the correlation between the various Koine Greek verb forms and the New Testament genres as defined by Aune. We used an ANOVA approach based on the SBL Greek New Testament (SBLGNT). Our proposed methodology analyses the covariance of tense, voice, mood and person across the different genres. Our experimental results show that the Aorist, Present, and Imperfect tenses, and the third-person form are significant in the analysis. This quantitative approach supports well-known qualitative theories of New Testament genre. It is important to note that mood and verbal aspects are irrelevant in the mainstream genre classification. Our findings suggest that the Greek verbal system is a reliable indicator of macro genre in the New Testament.

The authenticity of the Additamentum Aldinum (Sil. Pun. 8.144–223) has long been a matter of debate. While many scholars have expressed doubts that it is by Silius and suggest rather that it is from the hands of a skilful humanist, it has not, up to this time, been possible to provide solid evidence to support their intuition. This paper not only re-examines the standard arguments for and against authenticity but brings the latest computational stylometric techniques to bear on the question. These analyses reveal that the style of the Additamentum differs in statistically significant terms from the rest of Silius’ Punica.1

The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e. authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Both studies confirmed that this task is quite challenging. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style.

This study systematically compares four prominent Chinese translations of Plato’s Republic against two authoritative English benchmark translations, addressing a critical gap in existing Platonic scholarship: the lack of objective comparative analysis of its diverse Chinese versions. Using a three-tiered computational stylistic framework comprising macro-stylistic positioning, micro-level syntactic and lexical features, and cognitive-linguistic profiling via LIWC categories, we analyzed a purpose-built parallel corpus of 935,861 tokens. The corpus comprises four influential Chinese translations (Gu Shouguan 2010, Guo Binhe 1986, He Xiangdi 2021, Xie Shanyuan 2016) and two English benchmarks (Allan Bloom 1968, C. D. C. Reeve 2004). Significant stylistic divergences emerged among the Chinese versions and in contrast to the English texts. Chinese translations consistently exhibit a pronounced tendency toward explicitation, marked by significantly higher frequencies of cognitive process words (13.7%–16.4% vs. 11.2%–12.7% in English versions, d = −1.75, p < .001) and elevated frequencies of logical connectives, particularly result and contrast markers. Notable variations in syntactic complexity were also found, ranging from Guo Binhe’s concise style (17.985 tokens per sentence) to Gu Shouguan’s elaborate style (27.419 tokens per sentence), with substantial variation among Chinese translations (η 2 = 0.18–0.48). These findings underscore that the Chinese Republic translations are not mere linguistic variants but unique intellectual projects shaped by deliberate interpretive strategies and cultural contexts, highlighting translation’s active role in philosophical re-creation and re-localization. One Book, Four Different Voices: Using Computer Analysis to Uncover the Stylistic Fingerprints of Chinese Translations of Plato’s Republic Plato’s Republic is a cornerstone of Western philosophy, but how does it read in another language? In China, students and scholars face a “paradox of choice” with over ten different translations available. This study uses computer-based analysis to compare four influential Chinese translations by Gu Shouguan 2010, Guo Binhe 1986, He Xiangdi 2021, and Xie Shanyuan 2016, against two English benchmark versions. We analyzed nearly 936,000 words, examining sentence length, word types, and how translators handle key philosophical terms. The results reveal notable variations. Gu Shouguan’s version tends to use extremely long, complex sentences averaging over 27 words, aiming for a formal style. In contrast, Guo Binhe’s translation tends to use shorter sentences averaging about 18 words, making Plato’s ideas more accessible. The Chinese translations tend to make Plato’s logical arguments more explicit than the English versions, often adding connecting words like “therefore” and “because” to guide readers. They also tend to use more words expressing contrast and result, while English versions rely more on words signaling reasons. These stylistic choices appear to reflect the translators’ unique backgrounds, the historical eras spanning from 1986 to 2021, and their different goals. This research suggests that a single classic can become multiple, distinct works in translation, each offering a different window into Plato’s thought for Chinese readers.

Online Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure. The majority of studies on automatic fake news detection are restricted to English documents, with few works evaluating other languages, and none comparing language-independent characteristics. Moreover, the spreading of deceptive news tends to be a worldwide problem; therefore, this work evaluates textual features that are not tied to a specific language when describing textual data for detecting news. Corpora of news written in American English, Brazilian Portuguese, and Spanish were explored to study complexity, stylometric, and psychological text features. The extracted features support the detection of fake, legitimate, and satirical news. We compared four machine learning algorithms (k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) to induce the detection model. Results show our proposed language-independent features are successful in describing fake, satirical, and legitimate news across three different languages, with an average detection accuracy of 85.3% with RF.

The emergence of large language models (LLMs) has significantly advanced natural language processing (NLP); however, their capacity to generate human‐like content introduces serious security concerns. In particular, the misuse of LLMs for disinformation and impersonation on social media platforms such as X creates new opportunities for large‐scale manipulation and deception of users. This study aims to conduct a comprehensive investigation to (i) understand the distinct stylistic features effectively mimicked by 10 different LLMs and (ii) distinguish between LLM‐driven and human authors when LLMs are explicitly instructed to mimic a specific human writing style. In particular, we design adversarial prompts to mimic the writing style of human authors based on key stylometric features (quantitative analysis of writing style) and assess the mimicking effectiveness of different LLMs through extensive statistical testing. In addition, we conduct a survey that gauges human ability to recognize the author of a text and train machine learning models to identify human‐ and LLM‐driven authors, focusing on scenarios where specifically crafted adversarial prompts are employed to facilitate style impersonation. Our findings demonstrate that, when explicitly instructed, LLMs can effectively replicate features of human writing style. In addition, the survey results indicate that it is challenging for the participants to distinguish between the different types of authors. In fact, the participants only demonstrated a classification accuracy of 15% in correctly identifying the text generated by the LLM. In contrast, high detection performance is achieved only when the training data incorporate adversarially generated LLM samples produced using impersonation‐oriented prompts. Under this threat‐model–aligned training regime, stylometric‐based classifiers exhibit strong discriminative capability, attaining classification accuracy of up to 99% in distinguishing human‐authored text from LLM‐generated authorship.

Fake news is being generated in different languages, yet existing studies are dominated by English news. The analysis of fake news content has focused on lexical and stylometric features, giving little attention to semantic features. A few studies involving semantic features have either used them as the inputs to classifiers with no interpretations, or treated them in isolation. This research aims to investigate both thematic and emotional characteristics of fake news at different levels and compare them between different languages for the first time. It extends a state-of-the-art topic modeling technique to extract news topics and introduces a divergence measure to assess the importance of thematic characteristics for identifying fake news. We further examine associations of the thematic and emotional characteristics of fake news. The empirical findings have implications for developing both general and language-specific countermeasures for fake news.

The digitization of texts and the advent of big data analyses have transformed our understanding of authorship and collaboration in early modern drama. However, these advancements ought to be carefully contextualized within the material realities of early modern playwriting. The scarcity of surviving dramatic manuscripts underscores the significant role of agents like compositors, printers and editors, and the loss of the majority of plays from English commercial theatres casts doubt on the reliability of comparisons based on unique or common verbal parallels. The article focuses on drama from the Elizabethan and Jacobean periods, particularly the recently proposed collaboration between Christopher Marlowe and William Shakespeare on the Henry VI plays. Applying the IT concept of GIGO (garbage in, garbage out), it highlights the impact of textual transmission intricacies on authorship attribution, emphasizing that even the most sophisticated attribution techniques are only as reliable as the (often unreliable) data they utilize. Digitalizacija besedil in analiza velikih količin podatkov močno vplivata na naše razumevanje avtorstva in soavtorstva zgodnjenovoveške dramatike. Ob izjemnem razvoju, ki ga to prinaša, pa je treba upoštevati tudi materialne danosti nastajanja zgodnjenovoveških gledaliških iger. Pomanjkanje ohranjenih dramskih rokopisov nas opozarja na pomembno vlogo posrednikov, kot so stavci, tiskarji in uredniki; ker pa se je izgubila tudi večina dram iz tedanjih angleških komercialnih gledališč, so primerjave, ki temeljijo na odkrivanju edinstvenih ali pogostih besednih zvez pri različnih avtorjih, nezanesljive. Članek se osredotoča na dramatiko iz elizabetinskega in jakobovskega obdobja, zlasti na nedavno predlagano sodelovanje Christopherja Marlowa in Williama Shakespeara pri igrah o Henriku VI. Z vpeljavo koncepta GIGO (garbage in, garbage out) iz informacijske tehnologije opozarja na vpliv, ki ga imajo zapleteni procesi nastajanja in prenosa besedil na avtorstvo, in poudarja, da so tudi najbolj izpopolnjene tehnike ugotavljanja avtorstva zanesljive le toliko, kolikor so zanesljivi (pogosto nezanesljivi) podatki, ki jih uporabljajo.