AI-generated memetic warfare uses the appeal of memes and the distributive power of social media to reach and influence a wide audience, a tactic seen widely used in the first few months of the 2026 conflict among Iran and Israel and the United States. By analyzing a series of viral videos generated in the same style as the Lego Movie franchise in support of Iran, this paper explores the implications of this new technology on the way we wage war as well as on global economies, the environment, and societal health. I examine the distinctions among propaganda, “slopaganda,” and legitimate protest as well as the relationship between propaganda and social media platforms. I suggest how the humanities may serve not only to resist the unwanted effects of propaganda but also to engender the more desirable empathetic identification and understanding of legitimate protesters, no matter the media they use to draw attention to their cause.

This article argues that AI-assisted intertextuality should be judged by two linked capacities: the capacity to detect textual relations and the capacity to explain why those relations matter. Its originality is methodological rather than infrastructural: it proposes a constrained division of labor in which deterministic tools discover candidate textual relations, retrieval-augmented generation (RAG) explains them, and philologists adjudicate the claim. The test case is Coptic monastic literature, especially the writings of Shenoute and Besa, leaders of the White Monastery federation in late antique Egypt. Their works are difficult for non-specialists because Coptic requires segmentation, biblical quotations are often transformed, and scriptural language functions as social authority rather than ornament. Building on Miyagawa’s TRACER-based dissertation and recent THOTH.AI experiments, the article compares TRACER, passim, and RAG. TRACER and passim remain necessary for reproducible large-scale discovery of quotations and near quotations. RAG-based AI contributes differently: it can retrieve lexical and textual evidence, translate and segment Coptic, explain altered wording, and make allusive hypotheses explicit. The article proposes a conservative hybrid workflow: deterministic tools discover candidates, RAG explains them under system-level constraints, and philologists validate the final claim.

Semantic prosody describes the affective meanings or connotations of a linguistic element, which are commonly understood in terms of positive and negative alignment. Under the view of Construction Grammar, not only individual words but larger linguistic structures may carry semantic and pragmatic meaning, including positive or negative semantic prosody. The present pilot study takes this approach as a point of departure to investigate LLMs’ sensitivity to subtle pragmatic meanings in the form of semantic prosody. 4 state-of-the-art LLMs as well as a human participant group were tasked with rating the connotation and pleasantness of the go-around-Ving construction said to carry negative semantic prosody as well as two syntactically parallel constructions with neutral/positive semantic prosody. Results showed that while LLMs exhibit some sensitivity to constructional semantic prosody, their rating behavior differed significantly from humans when pooled into one group. Compared to humans, LLMs gave higher median and mean connotation and pleasantness ratings while also assigning less neutral ratings across constructions. Although not generalizable due to the exploratory character of this study, the results demonstrate that subtle pragmatic phenomena like semantic prosody represent a promising research area when it comes to discerning LLM language abilities and how they compare to humans’.

This study employs computational stylometry to empirically trace the evolution of Alfred, Lord Tennyson’s poetic style across three chronological phases: his early lyrics, his middle period centered on In Memoriam A.H.H., and his late epic cycle, Idylls of the King. To investigate these stylistic shifts, the research adopts a hybrid methodology that combines macroscopic distant-reading techniques (using the stylo package in R for Cluster Analysis and PCA) with granular linguistic profiling (using a transformer-based NLP pipeline in Python). The macroscopic analyses reveal a stylistic rupture in the late period; this demonstrates that Tennyson’s epic voice represents a structural departure from the stylistic foundation shared by his early and middle-period works. The Python-based analysis further shows that this late epic style was achieved through an active syntactic reconstruction characterized by increased sentence length, extended dependency distances, a shift from adjectival description to action-oriented verbs, and a heightened density of archaic diction. In contrast, the analyses identify the middle period as a peak in lexical diversity. It is marked by an objective, philosophical tone operating within a regularized syntactic framework. In conclusion, by translating literary concepts such as “lyric” and “epic” into quantifiable linguistic metrics, this digital-humanities approach provides robust empirical evidence that supports and refines traditional critical understandings of Tennyson's dynamic artistic development.

Large language models ( LLMS) have made language technologies widely accessible to humanities scholars, but they have also intensified concerns about transparency, reproducibility, and interpretive responsibility. This article argues that graph-based meaning representations, especially Abstract Meaning Representation (AMR) and Uniform Meaning Representation (UMR), can function as interpretive infrastructure for humanities research in the age of LLMS. Meaning graphs are “thin” by design in that they deliberately encode a constrained set of semantic distinctions. That selectivity is not a weakness for humanistic inquiry; rather, it enables a disciplined workflow in which researchers can separate (i) the semantic commitments that a text licenses (events, participants, temporal and modal dependencies) from (ii) richer interpretive claims (stance, ideology, affect, narrative framing) that can be layered on top. I review AMR and UMR at a level accessible to humanities audiences, discuss what changes in the LLM era (including both opportunities and limits of using LLMS for semantic parsing), and propose humanities-centered workflows and research questions. Several compact sample analyses illustrate how meaning graphs can support interpretive tasks in historiography, narrative analysis, and translation studies. A final section explicitly lists resources (datasets, tools, and guidelines) to support reproducible experimentation.

Before K-pop fanfiction tells stories about idols, it teaches fandom how to read them. This article examines that work as narrative labor, the collective transformation of public idol materials into shared conventions of character, relation, and affect. Rather than treating fanfiction as a derivative extension of official content, it asks how fans make idol personae legible through names, gestures, bodily signs, pairings, and recurrent scenes. Drawing on official and paratextual materials and a curated corpus of approximately 1,350 K-pop fanfiction texts, the study combines computational text analysis with interpretive reading. The analysis shows that fanfiction does not simply reflect idol images. It reorganizes them into relational scripts and affective archives that structure recognition, intimacy, hierarchy, desire, and memory. By reframing K-pop fanfiction as an affective infrastructure of legibility, the article contributes to K-pop studies, fanfiction studies, and digital humanities. It also offers an account of fan narrative labor before generative AI became a normalized condition of cultural production, clarifying what platform and AI systems encounter, simulate, or reorganize.

This paper explores how artificial intelligence is transforming engagement with cultural heritage from static preservation toward interactive knowledge production. Drawing on media-historical perspectives, it proposes the concept of knowledge liberation to interpret successive stages in the evolution of knowledge environments, from oral transmission and print culture to digital networks and AI-mediated interaction. Within this framework, cultural knowledge becomes progressively less constrained by the material conditions of its transmission. The paper examines several AI-enabled platforms developed at Peking University that support large-scale digitisation, structured data extraction, knowledge-graph construction, and multimodal cultural content generation. It argues that AI is emerging as a new knowledge medium that reshapes research methodologies, expands modes of cultural representation, and strengthens connections between humanities scholarship and public knowledge production.

This article presents the design and implementation of Digital Korean Studies (DKS) in the Korean Studies department at the University of Vienna, illustrating how digital humanities methods are integrated with the usual curriculum that teaches Korean language, culture, and history to transform both teaching and scholarship. It traces the department’s evolution from a traditional Korean Studies program into a modern, scaffolded curriculum that now also includes transferable digital competencies through teaching Digital Humanities methods that are adapted to Korean Studies students’ interests. The BA curriculum has added Digital Korean Studies courses that introduce the basics of Digital Humanities approaches and methodologies and let’s students absorb this through various hands-on and project-based assignments. The MA curriculum introduces more advanced tools and theoretical grounding, especially in the realm of textual analytics and social network analysis.

This article examines the methodological transformation that AI’s entry into literary criticism has set in motion. This transformation proceeds along four interrelated dimensions—technological capacity, critical method, literary ontology, and paradigm formation—that do not unfold in linear sequence but are mutually constitutive. At the technological level, the shift from machine reading to AI reading has given literary studies a new capacity for large-scale semantic analysis, though a qualitative gap persists between AI “reading” and human reading. At the methodological level, computational analysis has expanded the scale and verifiability of criticism without being equivalent to “objectivity”; its value lies in rendering the research process more transparent and reproducible. At the ontological level, generative AI poses substantive challenges to such core categories as “author,” “text,” and “literariness,” yet this disruption extends, rather than originates, the destabilizing work already undertaken by twentieth-century literary theory. Building on these three interconnected transformations, the article proposes the paradigm of computational literary criticism,” distinguishing it from Franco Moretti’s “distant reading,” Matthew Jockers’s “macroanalysis, and the broader field of digital humanities. The article argues that the core value of computational literary criticism lies not in replacing interpretation with computation, but in constructing a collaborative framework of sustained interaction between computational discovery and humanistic interpretation—a framework whose viability depends on methodological self-discipline, data ethical awareness, and an unwavering attentiveness to the humanistic core of literary inquiry.

This paper examines contemporary society through the concept of algorithmic hyper-late modernity, a new phase emerging from the convergence of digital technologies and global mobilities. Rather than viewing the digital revolution as a purely technological development, it conceptualizes it as a profound transformation of social systems, everyday life, and modes of human existence. Drawing on both classical and contemporary sociological theory, the paper situates this transformation in relation to earlier phases of modernity and late modernity, engaging with thinkers such as Durkheim, Giddens, Jameson, and Elliott. It further analyzes how social media platforms, algorithmic infrastructures, and artificial intelligence increasingly mediate political processes, public opinion, affective dynamics, and risk. The paper also advances the concept of Mobilities 3.0 to describe a condition in which mobility becomes deeply entangled with digital systems, generating algorithmic forms of movement, communication, subjectivity, and governance that operate across national borders. The paper concludes by arguing that human life is now constituted within networks linking humans and technologies, rather than in opposition to them. In this context, the humanities play a crucial role in critically reflecting on these transformations and in articulating ethical boundaries for human life in the age of artificial intelligence.

Citations

• GENAI, PROTEST, AND PUBLIC DISCOURSE: THE ROLE OF THE HUMANITIES IN RESISTING SLOPAGANDA
  Sarah Eilefson
  Journal of Humanities and AI.2026; 1(2): 18.     CrossRef
• Algorithmic mobilities and tourism geographies: movement, agency and power in hyper-late modernity
  Hideki Endo
  Tourism Geographies.2026; 28(4): 916.     CrossRef

This article is an invitation to discuss a recurrent question in the field of Humanities: “Does it make any sense to talk about Humanities in the age of generative AI?”. While many papers have been devoted to this topic, this paper brings together three distinct disciplinary viewpoints, namely the humanities, computer science, and education, to examine it from complementary angles. It discusses labor market transformations, ethical issues related to authorship and plagiarism, cultural bias, as well as the effects of AI on students' cognitive processes and learning practices. It also considers how these issues are being reshaped in current expert discussions. From these three perspectives, the analysis suggests that profound changes and transformations are inevitable, and the Humanities are no exception. In this context, it becomes necessary to distinguish between those tasks that cannot be replaced by artificial intelligence and those that are likely to be progressively taken over by it.

The date of a historical document, if it was published, can be recognized through the date of its preface or copyright page. However, dating an unpublished manuscript is much more difficult. In the case of old Hangeul documents, various linguistic features can be used to estimate the approximate date of the document, but as the number of features increases, the task tends to go beyond the purview of an individual human researcher, and become more appropriate to AI. This paper shows how artificial neural networks can be trained to estimate the date of documents using material whose date is known. For this purpose, various kinds of neural networks are examined: Bag-of-words model, CNN, RNN and Transformer. In addition, these models can be further sub-divided: unigram or bigram, character(syllable)-based or grapheme(phoneme)-based. After trained on documents with known dates, these models are applied to new (unseen) data, and the results are evaluated.