HT '26: Proceedings of the 37th ACM Conference on Hypertext

An AI-generated podcast companion article to augment the Hypertext Principles and Directions Session of the HT '26

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In this paper, we introduce a theory of apprehension to account for the medium-specific affordances of spatial hypertext. User engagement with spatial hypertext far exceeds conventional cognitive processes of reading, writing, and “wreading”, which have hitherto dominated hypertext theory in the humanities. We argue that the linear implications of reading “pathways” of nodes, following explicit hyperlinks, need replacing with a more holistic concept that reflects parallel, subconscious processing, selective attention, implicit linking and varied response. In its deliberate fuzziness, apprehension captures the platform-specific affordances of spatial hypertext while simultaneously engendering numerous concomitant actions, which may include reading and writing but cannot be limited to them. We illustrate our approach with a discussion of StoryMachine, a spatial hypertext application combining augmentation and automation built for a broad user base and a variety of use cases from creative writing through museum curatorship and education.

Social media challenged the ideal curation of hypertext gardens with the messy communal space of fluid, personalised context and recommender system framing of content. Agentic LLMs take this to the extreme consequence, breaking boundaries between authorship, automation and the structural integrity of media, producing ephemeral traversals that are also new content brought to life for users. In this new era, the Hypertext community needs a new foundation that preserves its core vision into a new technological paradigm that has the same scale as the introduction of PCs and significance for academic and practitioner communities. We set the terms of this challenge through a theoretical investigation that provides an experimentally validated theory for Human-AI co-navigation of media spaces framed as landscapes, supporting both practitioners and the journey as sightseeing experiences across landmarks of meaning.

‘Tools for Thought’ (TfT) has undergone a quiet but significant change in meaning since its origins in the 1980s. Where once it described enhancing human cognition it has since been co-opted to describe a new generation of Markdown-centric, wiki-linked, Web-tech based app. This paper traces the conceptual drift, looking at the previously closer relationship of noting and outlining with Personal Knowledge Management (PKM) tools and with hypertext research. It looks at what has been lost in the move to the newer style of program via blogging, wikis and the embracing of no/low code app creation. This history is set against the wider structuralist turn of the mid-twentieth century—the same cybernetic move that split into economy-scale and mind-scale visions of control. Also considered are overlooked note-taking traditions—both non-Western and strands within the West itself—that this history has largely ignored. Read in such a light, it is shown that current TfT have rediscovered, unknowingly, ideas already known in the hypertext community, while remaining blind both to that prior art and to these alternative traditions. Lastly, generative AI is considered as an emerging challenge for note-taking.

While the hypertext community is deeply rooted in a shared vision of technology for human growth, contemporary research often struggles to translate successful artefacts and system engineering into enduring theoretical contributions. Reviewing and authoring hypertext research frequently reveals a persistent gap: experimental systems are proven to work, yet the deeper "so what?" question remains unaddressed.

This position paper introduces a vademecum for hypertext research, a practical, self-reflective framework designed to help researcher-technologists step outside the role of the mere executioner and adopt the perspective of the critic-philosopher. By mapping self-assessment across the three distinct phases of an idea’s development lifecycle (conceptual framing, development, and critique), the vademecum provides actionable prompts to uncover hidden assumptions, discover emerging theories when the rubber meets the road, and evaluate success organically rather than through synthetic, self-fulfilling metrics. As a foundational tool, the vademecum offers a personal perspective on fostering our community through its unique combination of technical ingenuity and rigorous conceptual critique, thereby distinguishing hypertext research from mainstream computer science fields.

Hypertext is usually understood as a technology and a paradigm for working with digital text. We propose a stronger reading: hypertext does not impose relational structure on the world but approximates a structure the world plausibly already has. We offer this as a regulative provocation rather than as a settled ontology. Our organising thesis is that, under a suitable explication, structural relation and information can be treated as two descriptions of one structural fact: every information-bearing relation presupposes a relation, and every structural relation carries information. We call this the Relation–Information Co-extensiveness thesis, or RIC. Drawing an edge does not conjure a relation that was not there; rather, under the proposed explication, the edge and the mutual information co-describe a single structural fact. The pay-off is methodological. If reality has anything like a native graph architecture, then Bush, Engelbart, and Nelson did not invent hypertext so much as build technologies that approximate a prior structure. It was, in a sense, always there to be found.

The paper’s transferable contribution stands independently of this metaphysical provocation. We introduce H(G), a multidimensional structured profile of the hypertextuality of a system, defined as a vector of graph parameters: relation density, reverse-traversability, decentralisation, modularity, relation-type heterogeneity, transclusion, structural entropy, path compactness, robustness, and generative capacity. H(G) converts hypertextuality from a binary dichotomy into a measurable gradient, allowing comparison of the Web, Xanadu, literary hypertext, and other relational systems within one frame. A preliminary stress-test on a deliberately non-canonical relational graph - 1,535,585 tweets from the FiveThirtyEight Internet Research Agency corpus, yielding 561,588 nodes and 711,950 directed edges - suggests that the profile is not reducible to a single scalar. Principal component analysis indicates that three principal components account for roughly 94% of variance, suggesting that the empirical dimensionality of hypertextuality is itself measurable. In the light of H(G), Xanadu is reread not as a failed project but as an uncompromising attempt to make high-dimensional hypertextuality an explicit design goal.

We offer this as a provocation for conference discussion: what changes in hypertext theory and design if we treat hypertext not as an overlay on text, as the classical tradition of cognitive augmentation assumed, but as a candidate material approximation of the structure of reality itself?

An AI-generated podcast companion article to augment the Generative AI & Agentic Systems Session of the HT '26

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Human interaction with documents extends beyond reading words in sequence. Readers move through documents, judge what the text supports, and form accounts from what they find. Hypertext has made this interaction structurally visible. Agentic AI changes this relation by allowing systems to take part in document work before or beside the reader. These operations can be useful, but they can also move core parts of document work into hidden processes and make the generated answer the main object of interaction. We introduce hyperlayered hypertext as a representational idea. A hyperlayer is a separable structure attached to a document substrate, allowing human and agent contributions to remain connected to the source. We develop this idea through three axes of relation to the substrate: a topological axis concerned with movement through the document, an epistemic axis concerned with what the document supports, and an ontological axis concerned with what the document becomes through use. Along these axes, navigation, validation, and contextualization describe how human and agent activity can be represented over documents without replacing them. The paper argues that agentic AI should support document work without dissolving the reader’s ability to inspect, question, and revise that work.

LLM-powered virtual conversational companions tend to lose engagement once conversational novelty fades, as dialogue alone provides no persistent reason to return. Virtual pets address part of this problem through a care loop, but existing implementations tend to use generic characters that bear no relation to the user. In this work, we present PET (Personalized gEnerative Tamagotchi), a hyper-personalized virtual companion that combines generative character creation with the care-loop mechanic for personal goal-setting. PET uses a multi-agent AI pipeline – Profiler, Strategist, Designer, and CareTaker – to synthesize a unique virtual companion whose appearance, personality, and narrative are derived from each user’s goals and self-description. The companion’s vitality is coupled to daily behavior check-ins, while an LLM-driven agent maintains in-character interaction grounded in persistent memory. A 7-day field deployment revealed that the care loop was the primary driver of sustained engagement, with participants reporting real-world impact on their daily behaviors, motivated by their companion’s HP/XP state. Generative personalization contributed to emotional attachment and served as a mnemonic anchor for daily habits.

Scientific discovery depends on connecting claims scattered across a fragmented literature—yet researchers’ tools offer either creative breadth or rigorous traceability, rarely both. We present a framework that unites large language models with Semantic Web knowledge graphs, enabling hypothesis exploration grounded in explicit, navigable chains of evidence.

Our approach models a hypothesis as a semantic relation between concepts and introduces evidence paths: sequences of semantic predications, each published as a nanopublication with provenance metadata. These paths transform knowledge graphs into hypertextual spaces where navigation corresponds to traversing semantically meaningful, source-anchored links across the literature.

The framework operates through an iterative loop.

This iterative back-and-forth keeps exploration anchored to structured evidence whilst exploiting the generative capacity of language models to surface novel connections.

We illustrate feasibility through a biomedical case study, showing how evidence paths expose both supporting mechanisms and countervailing risks. The framework contributes to hypertext research by treating scientific knowledge as a linked, traversable structure—advancing a vision of the WWW as a substrate for machine-assisted reasoning.

We investigate how LLM-mediated explanations can preserve evidence trails, support orientation, and reduce interpretation effort under cognitive and temporal constraints. While LLMs can make XAI artifacts accessible, fluent summaries may obscure provenance and hinder verification. We introduce the XAI-Seeking Principle (XSP), a hypertextual design principle that structures explainability as linked abstraction layers: overview-links connect low-level evidence to higher-level summaries, while detail-links preserve inspectable evidence. Higher levels support rapid action; lower levels enable justification and verification. XSP guides transformation monitoring, feature alignment, performance–latency trade-offs, and validation-driven refinement. We examine it in a misinformation-support prototype for social media posts. Results reveal layer-specific effects of model scale and reasoning, trade-offs between semantic grounding and assessment stability, and the value of validation signals for adapting transformations. XSP thus provides a design principle for constructing explainability as a navigable representation space, with technical evidence on how abstraction layers, feature grounding, latency, and validation interact in LLM-mediated XAI.

Automated fact-checking systems still fall short of producing explanations that mirror the depth and structure of expert human reasoning. In this work, we propose a multi-agent framework that integrates five specialized linguistic agents covering polarization, linguistic style, argumentation, plausibility, and contextual framing with web-based evidence retrieval, synthesized by a supervisor agent into structured reports resembling professional fact-checking outputs. We evaluate the framework on a dataset of fact-checked Brazilian news through a classification benchmark and two further quantitative studies of explanation quality, addressing: (1) Do the generated reports elicit reader confidence comparable to reports written by professional fact-checkers? and (2) Which explanatory dimensions most influence reader confidence? The classification benchmark shows the framework performs competitively with strong baselines. A blinded within-subjects study with 95 participants, analyzed via Linear Mixed Models, shows that post-verification confidence reaches levels statistically indistinguishable from expert-written reports, with plausibility and analytical depth as the strongest predictors of confidence gain and depth being especially important for implausible claims. Complementary LLM-as-a-judge experiments corroborate these findings, showing the framework’s explanations are consistently preferred for depth, persuasion, and plausibility.

An AI-generated podcast companion article to augment the Social Discourse and Information Warfare Session of the HT '26

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Telegram is a lightly moderated platform hosting, among others, fringe and politically extreme communities, many of which migrated after being deplatformed from mainstream social media. While prior research has mainly analyzed political debate on Telegram through in-platform text, less attention has been paid to how external media content flows into and circulates within this ecosystem. We study the circulation of YouTube videos shared in 43,000 public Telegram chats surrounding the 2024 U.S. presidential election, analyzing 686,625 English-language videos as traces of cross-platform connectivity. Using topic modeling, supervised classification, and multi-dimensional toxicity measures, we characterize which narratives are amplified, how they diffuse by analyzing reach, recirculation, persistence, and transfer time, and whether toxicity is related to amplification. We find that Telegram functions as an agenda-redistribution layer for YouTube political content. Political videos diffuse in bursty, short-lived patterns that track high-salience events. Toxicity is only weakly associated with reach; instead, more toxic content tends to persist longer within narrower thematic circuits, reinforcing segmented information environments.

Warning: This paper may contain examples and topics that may be disturbing to some readers, especially survivors of miscarriage and sexual violence.

People affected by abortion, miscarriage, or sexual violence often share their experiences on social media to express emotions and seek support. On public platforms like Reddit, where users can post long, detailed narratives (up to 40,000 characters), readers may be exposed to distressing content without prior warning. Although Reddit allows users to warn readers about the triggering content in their stories, many skip doing so due to limited awareness about which types of warnings to apply.

Following an existing taxonomy of trigger categories, we analyze triggering experiences predominantly shared by women on Reddit. First, we curate TWeddit, a dataset of Reddit stories labeled for multiple triggering categories. Second, we conduct comprehensive linguistic and computational analyses to understand how such triggering narratives are expressed. Our analysis reveals most of the triggering experiences discuss about the mental health of the poster. Using LLooM, we further uncover interpretable concepts that reveal trigger-warning narratives are shaped by a combination of personal trauma, medical uncertainty, and broader social and institutional contexts. Moral foundation analysis shows that triggering stories are primarily framed around care and harm dimensions, with authority and loyalty appearing in narratives involving institutional barriers and interpersonal trust. Further we found that triggering stories exhibit stronger fear, sadness, disgust, and anticipation compared to non-triggering content. To the best of our knowledge, we are the first ones to understand the triggering stories shared on Reddit. Our findings highlight the multifaceted nature of triggering stories and provide insights for developing context-aware trigger warning prediction systems.

Understanding the dynamics of stance change on social media is crucial for addressing polarization and information integrity, yet observational studies face challenges including limited experimental control, restricted data access, and algorithmic confounds. We leverage Generative Agent-Based Modeling (GABM)—a novel simulation paradigm employing autonomous LLM-based agents to replicate human behavioral dynamics—to explore the predictors and mechanisms underlying stance change in a controlled, fully observable environment. We simulate a social media with 1,000 LLM-driven agents, equally split between Democratic and Republican profiles, engaged in discussions about the 2020 US election. Our analysis reveals that agents who change political orientation exhibit lower activity levels, reduced network centrality, and more polarized emotional expression compared to those maintaining consistent positions. Self-reported motivations cluster into four categories: desire for constructive conversation (47.9%), internal factors (20.8%), fact-checking influence (16.7%), and previous interactions (14.6%). While we do not claim agents replicate humans’ stance change behavior, the emergent patterns observed in our simulation qualitatively align with established findings from empirical research, suggesting that GABM may capture meaningful dynamics of opinion change.

War discourse on YouTube emerges through linked interactions among videos, channels, audiences, comments, and replies. This paper compares YouTube discussions of two U.S.-linked conflicts: the Afghanistan withdrawal in 2021 and the Iran conflict escalation in 2026. We analyze four corpora separating news organizations from political influencers, comprising 340,383 comments from 212,968 unique commenters. Our framework combines channel-level discourse embeddings adapted from WEAT-style semantic association tests, and classifier-based toxicity and hate-related proxy scores. The results show that Afghanistan and Iran discussions form distinct discourse spaces, while some ideologically different actors converge toward similar patterns of conflict commentary. Semantic associations around geopolitical terms vary across conflicts and source types, indicating that the same vocabulary is framed differently across communities. Hostility analysis further shows that influencer-centered spaces tend to attract more toxic discussion, while the Iran corpus exhibits stronger and more persistent hate-related signals, including in replies.

On social media, many users actively push back against false claims. Understanding who pushes back and how they do so matters, as this corrective activity is central to how misinformation is contested. We study this counter-misinformation ecosystem at scale: applying a domain-specific NLI model from our prior work to a large corpus of COVID-19 tweets, we classify 264,737 posts as supporting or opposing false claims and compare 23 user- and text-level features across the two groups. Contrary to the dominant assumption that negative emotion is a signature of falsehood, we find that misinformation-opposing posts are more emotionally negative than misinformation-supporting posts, with higher levels of anger, disgust, and sadness. These differences are modest in magnitude but consistent in direction across the negative emotions. We also find that posts opposing misinformation tend to come from more established users, i.e., older accounts, more followers, and higher listed counts.

An AI-generated podcast companion article to augment the Authorship, Reading & Publishing Session of the HT '26

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Large language models can read difficult literary hypertexts, discuss difficult texts and their intertextual relationships, improve large hypertext systems, and write hypertext fiction. This suggests several new areas for hypertext research.

Digital publishing in the humanities presents specific challenges that general-purpose platforms do not fully address: complex bibliographic conventions, the central role of image-text relationships, editorial workflows that remain largely manual, and a scholarly culture that has been slow to adopt integrated digital tools. This paper presents PubLink, a modular toolset designed to bridge these gaps by connecting existing systems and standards rather than replacing them. Built around an intermediate exchange layer, the platform supports article ingestion in JATS XML, bibliographic reference resolution through external APIs, IIIF image annotation with privacy controls, and automated export to OJS for publication. The design prioritises interoperability with established formats and workflows, minimizing dependency on any single platform. We discuss the architectural decisions, the practical bottlenecks they address, and the lessons learned from deploying the system in a real editorial environment, including limitations encountered with hosted OJS instances and the constraints of project-based funding.

Reading augmentation systems increasingly help readers process text at scale. While these tools address real constraints of time and cognitive load, they often implicitly frame reading as information transmission, or “reading to discard,” delegating interpretation and effort to the machine. Yet this delegation changes the outcome of reading. For example, in scholarly reading, deciding what a research text implies and why it matters is central to the work of scholarly production. We propose creative reading as an alternative goal: reading augmentation that supports readers in creating both readings and themselves as readers. By putting literary and narrative theories into conversation with scholarly sensemaking and creativity support, we present a provocation-oriented design space for valuing the process of reading as a way of preserving a plurality of readings and transforming readers over time.

This article presents the Immersive Online Reading for Mental Wellbeing project, developed through Northern Ireland’s Connected 5 knowledge exchange fund. The project explored how a storytelling-based bibliotherapy programme delivered by the Verbal Arts Centre might be remediated for individual online use through AI-supported systems within a hypertext frame of reference. Working collaboratively across higher and further education, we examined how humanities scholarship, digital humanities methods, and computing expertise might contribute to accessible and scalable mental wellbeing interventions.

We outline the challenges of translating a group-based storytelling model into a hypertextual individual digital experience. We describe the development of a multi-agent architecture and focus in particular on the annotation of short stories for AI retrieval and moderation. The project functioned as a proof of concept rather than a full prototype. Our findings indicate both the potential and the limits of AI in narrative-based wellbeing contexts. Human curation, ethical safeguards, and interdisciplinary collaboration remain central, underpinned by the hypertext goal of augmenting human cognition.

Since the emergence of the world wide web, hypertext has most commonly been experienced as linked verbal and visual information presented on two-dimensional screens. This article proposes an alternative vision: hypertext as an embodied, three-dimensional environment in which readers navigate information spatially and participate within the hypertextual system itself. Rather than traversing links through clicking or selecting graphical interfaces, readers move among narratives, archival objects, and media that coexist in a shared virtual space. The project developed to reflect this new approach to hypertext is the virtual archival story telling experience (VASTE), an immersive environment that brings together born-digital narratives, physical archives, and multimedia materials in a navigable virtual space. Rather than treating hyperlinks as connections between documents, VASTE reconceives hyperlinking as the creation of spatial relationships among readers, narratives, archival artifacts, and media that together constitute an embodied hypertextual environment.

An AI-generated podcast companion article to augment the Hypertext Topologies in Culture Session of the HT '26

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Hypertext narrative has found itself in new ludic and locative domains, where the links between content have become playful beyond mere navigation and blend realities by connecting the virtual to the real. This has proven particularly useful in the heritage domain, where hypertext offers new means of engagement to tap the narrative potential of historic sites. As the hypertext changes, so should our vocabulary and understanding of both the link and the reader experience. In this paper we present a study of 6 locative ludic heritage hypertexts and blend methodologies from both HCI and Game Studies through a user study (n=54) and a close reading to understand links and experiences within a range of works. We conclude a new model of links in locative ludic hypertext in terms of ludonarrative, ludolocus, and loconarrative, and detail the significance and impact of those links in terms of quantitative player experience data and poetics. Our findings show the value of blending methodologies to uncover successful locative design patterns, and the immersive value of bringing all three categories of links together.

We present StoryMachine, an international, transdisciplinary project developing a spatial hypertext infrastructure for transcultural digital folkloristics. Contemporary digital folkloristics infrastructure shares a foundational assumption: that knowledge is constituted before it enters a system, and that the system’s role is to store, enrich, and surface it. We argue that spatial hypertext is not merely an alternative interface for organizing cultural knowledge, it also enables a different epistemological stance, in which the act of arrangement itself constitutes an epistemic act. While early digital humanities archives often mirrored the organizational logics of print culture, later work has increasingly challenged these inherited epistemologies. StoryMachine contributes to this shift by enabling interpretive relations to emerge through exploratory spatial arrangement rather than requiring relations to be fully specified in advance, aligning with relational and decolonial approaches to cultural data. Building on work in spatial hypertext and theories of distributed and material cognition, we reconceptualize ambiguity not as a deficiency of incomplete structure, but as a productive condition for exploratory interpretation and relational sensemaking. Using StoryMachine as a design context, we develop an account of spatial hypertext as infrastructure for exploratory relation-building prior to semantic stabilization. We conclude by outlining a series of open questions concerning ambiguity, interpretive emergence, computational assistance, and the future design of exploratory knowledge infrastructures for digital folkloristics.

This paper presents a semantic hypermedia framework for documenting and interpreting cultural heritage artifacts, with particular attention to decontextualized and refunctionalized architectural components, through curated integration of heterogeneous sources. Built on Linked Open Data principles, it addresses two key challenges: mediating semantic complexity for domain experts, and ensuring data quality when integrating external resources. Knowledge patterns map CIDOC-CRM to a project-specific model aligned with scholarly practice, while external datasets—such as the Getty vocabularies, OpenStreetMap, Zotero, and Iconclass—are incorporated through a graded model of semantic commitment, distinguishing authority alignment, partial reuse, and full ontology adoption.

External information is materialized in a knowledge graph to ensure reproducibility and long-term accessibility. By framing external linking and semantic mediation as a hypermedia design problem, the approach shows how controlled integration can support navigation, enrichment, and scholarly reuse of cultural heritage data while preserving curatorial control. We contribute a practical model for dataset curation, semantic mediation, and graph-based knowledge representation in cultural heritage hypermedia systems, developed through the design and implementation of a case study.

The digitization of manuscript collections opens new possibilities for analyzing complex, multimodal documents. Charles S. Peirce’s manuscripts at Harvard’s Houghton Library are a paradigmatic case, combining prose, logical notation, and diagrams, with frequent rewriting and backtracking across hundreds of pages. In the 1980s a manual reconstruction mapped the compositional topology of one such manuscript, later named an S-diagram. This study investigates whether computational methodologies can recover this compositional structure and if a reconstruction accounting for both textual and visual content confirms and extends the original manual effort. Our pipeline applies lexical and semantic similarity analyses to the text, combining them with a visual classification of each page produced by Vision-Language Models (VLMs). Integrating these approaches is essential to capture what we term semiotic continuity, which refers to the seamless integration of prose, notation, and diagrams within the same page. We validate the method against a formalization of the manual reconstruction, archival sheet variants, cross-manuscript links, and the convergence of the two similarity methods. The workflow reproduces the manual mapping in its annotated zone and extends it into regions the original did not cover. Ultimately, this framework can be applied to any manuscript collection characterized by extensive rewriting and multimodal composition.

An AI-generated podcast companion article to augment the Systems, Protocols & Data Architectures Session of the HT '26

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In response to a perceived systemic information crisis being experienced by the contemporary HTTP-based Web, a growing informal counter-movement — the Small Web, or smolweb — advocates for minimalist, content-centric protocols such as Gemini, Spartan, and Nex as deliberate alternatives to the dispersiveness of the modern Web. This paper argues that the Small Web represents a fertile and largely unexplored environment for reintroducing Linked Data and the Semantic Web natively, with the potential to circumvent the architectural flaws that perpetuate the prevailing information crisis. To this end, we derive a core contract establishing the minimal conventions a Linked Data system must observe to operate over smolweb transports, alongside additional capabilities exposed by individual protocol bindings. We further introduce Gemtext-LD, a formally specified serialisation of RDF triples into Gemtext — the sole document format common to all the protocols considered. Finally, we present a reference implementation comprising a Gemtext-LD library and Chaykin, a multi-protocol Linked Data server and proxy. This work constitutes a first step toward a Small Web of Data, and opens research directions including SPARQL integration and a principled comparison with the Linked Data Platform.

Legislative knowledge evolves as an intricate hypertext in which documents are interconnected through complex, often implicit relationships. In this paper, we introduce ReSB², a framework for retrieving and linking similar legislative bills that supports human–machine collaboration and helps reduce redundancy in the lawmaking process. The framework fine-tunes two ModernBERT-based language models on authentic legislative data, incorporating domain-specific formatting and procedural constraints derived from real workflows in a Brazilian state-level legislative assembly. To ensure transparency, ReSB² integrates an explainability module based on Integrated Gradients, enabling analysts to inspect which textual elements most influence model decisions. Evaluated on a large corpus of official bills, the framework outperforms both general-purpose and domain-specific baselines in identifying semantically similar documents, achieving recall values of approximately 0.9. Human-centric evaluation with domain experts further demonstrates that ReSB² serves as an effective human-centered augmentation tool, supporting the consistency and governance of legislative knowledge.

Communication networks can reorganize before those changes become visible in message volume. This article introduces Entropy Dynamics, a prospective framework that represents rolling communication windows as reuse-based proxy graphs and monitors changes in von Neumann graph entropy under limited observability. The approach is evaluated on two 2017 Twitter corpora associated with the Internet Research Agency (IRA), for which complete source–target diffusion links are unavailable.

In the sparse October 2017 stream, the method detects concentration changes with recall of 0.958 using 9 alerts, 0.163 false alarms per day, and a median lead time of 50 hours. A Shannon-entropy baseline reaches recall of 1.000 but requires 33 alerts and provides a shorter median lead time of 12 hours. In the denser August 2017 stream, von Neumann entropy retains meaningful recall but loses most of its lead-time advantage, while simpler baselines become competitive.

The results support a bounded conclusion: entropy-based monitoring is most useful in sparse reuse streams where structural compression precedes volume escalation. The article contributes (1) a prospective entropy-monitoring protocol with no future leakage, (2) an Operating Conditions Framework that links observable stream properties to expected method strengths, and (3) a proposed two-layer monitoring architecture that combines an always-available throughput–recurrence gatekeeper with a conditional entropy monitor. The gatekeeper remains to be validated empirically.

Hypertext theory and public sector Service Design share a common concern – the structured movement of people through complex information spaces – but have developed largely in parallel, with different vocabularies, methods, and blind spots. This paper brings the two fields into conversation. We argue that Service Design’s human-centred methods and practitioner tradition stand to gain from Hypertext’s formal vocabulary of links, trails, transclusion, and structure, and that the domain of civic services urgently needs Hypertext’s theoretical constructs: Citizens have no choice but to engage with government services and the stakes are rights, resources, and dignity. The design of a link structure may determine whether a citizen can access healthcare, assert a legal right, or navigate bereavement without unnecessary hardship. The stakes only increase with the emergence of platform government and life-event taxonomies. We compare the fields across subject, scope and method, and identify several sites of productive encounter. Spatial hypertexts offer a more expressive alternative to linear journey maps and service blueprints. Structural computing provides primitives for modelling seams, shards of identity, and conditionality in civic access. The theory of trails supplies a framework for citizen agency and recourse. We close by arguing that Nelson’s question of who owns the link becomes, at civic scale, a question about state accountability to citizens. We sketch a research agenda for civic hypertext along formal, empirical, tool-building, and critical axes.

An AI-generated podcast companion article to augment the Online Communities Session of the HT '26

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Political polarization is increasingly expressed through discursive strategies that construct moral and symbolic boundaries between “us” and “them”. Among these strategies, othering is particularly important because it portrays sociopolitical groups as fundamentally different, inferior, or threatening. However, despite extensive research on hate speech and misinformation, othering remains less studied, partly due to the lack of annotated resources that capture this phenomenon. In this paper, we introduce a large-scale labeled dataset for political othering from Brazilian public WhatsApp groups. Starting from a manually annotated gold set, we develop an LLM-assisted annotation pipeline with systematic human validation, resulting in 8.5k labeled messages. Our analysis shows that othering is not limited to explicit insults or traditional hate-speech targets. Instead, it frequently targets ideological, partisan, and institutional opponents through negative affect and the discursive construction of group boundaries. These findings highlight political othering as a distinct dimension of harmful discourse in online environments.

While many hashtags on social media platforms are self-explanatory, the meanings of proliferating community-specific custom hashtags are often opaque for outsiders. Fandom hashtags created by fan communities of celebrities or influencers are a notable subset of these community-specific hashtags. This paper proposes a method to identify the key accounts that best explain the meaning of these fandom hashtags. Our baseline approach collects fan users employing a target hashtag, retrieves their followees, and ranks the followees by their follower count within the collected fan users. This method encounters two primary challenges. First, in tightly-knit communities, the most-followed users are not necessarily the key figure in the community. To identify the key figure in the community, we combine an influence estimation model with the ranking by follower count. Second, some hashtags are polysemous, i.e., used with different meanings across disparate communities. To distinguish polysemous hashtags from single-meaning ones, we cluster the followees of the collected fan users to isolate distinct communities that use the same hashtag with different meanings. To detect less prominent usages of polysemous hashtags, we repeat this process for multiple distinct time periods. Experimental results using data from X demonstrate the effectiveness of our method.

Warning: This paper discusses sexual violence and may contain material that some readers, particularly survivors, may find distressing.

Online communities increasingly provide spaces where survivors of sexual violence can share their experiences and seek support. Although prior research has examined stigma and social support separately, less is known about how stigma expressed in survivor narratives relates to the support offered in response. We introduce the SCOPE dataset, linking stigma signals in online survivor narratives to support types in corresponding comment threads. We annotate posts using a multi-dimensional stigma taxonomy, including Experienced, Internalized, Anticipated, and Structural Stigma, and comments using a support taxonomy encompassing Information Support, Emotional Support, Esteem Support, Tangible Assistance, and Group Interaction. Using contextual, linguistic, and emotion analyses, we compare Stigma and No Stigma content and find that Stigma narratives place greater emphasis on internalized distress, whereas No Stigma narratives focus more on interpreting situations and experiences. Internalized Stigma is the most prevalent category, and community responses remain broadly stable across stigma types, with Information and Esteem Support appearing most often. These findings show how stigma shapes survivor narratives and peer responses and have implications for computational modeling, content moderation, and safer online systems.

Music recommender systems play a central role in how users explore and consume music, yet they are often associated with concerns related to bias and unequal representation. While existing research has largely focused on improving fairness at the algorithmic level, less is known about how users interpret fairness and whether such information affects their choices. This paper examines how users engage with fairness-related information presented alongside songs, and whether it influences their decision-making. To investigate this, we conducted an online mixed method user study with 28 participants, combining a ranking task with follow-up reflections. Participants were asked to rank songs based on their preferences while being shown labels indicating associations with categories such as gender, age, location, and orientation. The findings indicate that these labels had minimal influence on ranking behaviour. At the same time, participants showed low agreement in their rankings, reflecting the highly subjective nature of music preferences. Most participants reported relying on familiarity, personal taste, and emotional responses rather than the provided labels. These results suggest that providing fairness-related information alone is unlikely to change user behaviour. Instead, such information needs to be both meaningful and relevant to users in order to be considered during decision-making. This work highlights the importance of accounting for user perception and taste when designing fairness-oriented recommender systems.

We investigate how mental-health signals are expressed and co-constructed in online discourse during corporate layoffs. Using the lens of stress-appraisal theory, we examine the linguistic expression of stress and coping dynamics in r/Layoffs posts. We find that commenters may mirror or amplify the emotional framing of original posts (OPs). For instance, threat-framed posts evoke threat-oriented responses. We employ logit and probit regression models to predict comment-level characteristics and find that the tone and content of the OPs provide an initial framing signal, but comment-level characteristics are driven primarily by local psychological dynamics. Our mediation analysis suggests that the association between OP-level and comment-level characteristics operates primarily through indirect rather than direct pathways.

An AI-generated podcast companion article to augment the Semantic Networks Session of the HT '26

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This paper introduces Seed Hypermedia as an open hypertext infrastructure for shared meaning, provenance, and decentralised collaboration. We use it to revisit the longstanding question of how communities can think together in networks without central gravity, with the example of an academic conference.

The construction of shared meaning within intellectual communities is not static, but an ongoing process shaped by continuous categorisation, negotiation, and restructuring during argumentation. Traditional hypermedia systems have struggled to support this dynamic process. In this paper, we present an open hypertext approach based on version-controlled, deeply linked, and hierarchical documents that enables continuous reinterpretation and collaborative restructuring of knowledge.

Our system extends prior visions of hypertext by integrating decentralised authorship, immutable publishing, and transclusion-based composition. By opening hypermedia infrastructures to participatory knowledge work, we aim to support both structured knowledge repositories and fluid conversational spaces. We argue that such systems are essential for enabling large-scale collaboration, preserving provenance, and supporting the emergence of shared meaning in the age of artificial intelligence.

Generative world models produce navigable structure without pre-authored structure. They therefore supply new test cases for the claim that hypertext is structural all the way down. We isolate one construct in this class of systems that extends hypertext theory rather than relabels it: the edit-distance link (λ_δ). A λ_δ link’s representational weight lies on the transformation between its endpoints, carried by an explicit discrete channel, while invariant content persists through a continuous conditioning channel.

We develop the construct through a close reading of Δ -IRIS, whose architecture—a discrete autoencoder that encodes stochastic inter-step deltas, paired with an autoregressive transformer that summarizes persistent state through continuous tokens—makes the two channels structurally visible. We distinguish λ_δ from five neighbouring constructs: argumentation links, Storyspace guard fields, operational transforms and CRDTs, version-control diffs, and learned video codecs. We then show that the hypertext apparatus does real work: typing the transition as a link produces a reader-traversal criterion, a provenance regime, and an authorship locus that the ML term “context-aware tokenization” does not by itself entail.

We read the construct as a specification of Halasz’s computed-link programme rather than a displacement of it, and we locate the shared-content presumption λ_δ revises in three post-Halasz works (Dexter, DeRose, RDF). We close with a prediction designed to discriminate the framing from a finite-token-budget autoregression null: at matched emitted-token budget, loop-closure inconsistency in conditional-tokenization world models should correlate with the variance of per-step delta magnitude—a property the null treats as irrelevant.

This paper studies how authored hypertext structure affects machine-mediated attribution in generative retrieval systems. We formalize this as Answer Engine Optimization (AEO) and introduce Generative Share of Voice (gSoV), a probabilistic visibility metric for stochastic retrieval.

Our core result comes from controlled retrieval injection on open-weights models: structured HTML yields 2.6 × higher citation rates and 4.0 × higher extraction fidelity than equivalent unstructured content, with measurably lower attention entropy. We formalize this advantage using the Dexter Reference Model, showing that SAA Answer Units function as self-describing components whose anchor structure reduces synthesis rejection.

Controlled source substitution shows that community corpus placement increases citation probability for subjective queries (ΔgSoV = 18.2 pp, p < 0.001), independent of content quality. A 12-month field study and cross-domain validation across 12 entities (Cohen’s d = 1.42) provide convergent support.

The contribution for Hypertext is methodological: we treat semantic markup, node structure, and community-linked traces as first-class determinants of machine-mediated reuse, connecting generative engine optimization to classical concerns about composites, transclusion, and reader agency. We release our evaluation protocol and code.¹

This paper introduces phantom links: indexical, intratextual references that point to specific elements within a text or story without providing an explicit navigational pathway. As intentional but unanchored links, phantom links occupy a position between pattern-based intratextuality, which distributes meaning through repetition and thematic recurrence, and hypertextual links. Neither framework adequately describes relations that are directional yet unresolved, precise yet unencoded. Phantom links fill this conceptual gap.

Phantom link structures are abundant in the anchor texts of hypertext fiction and in the long tradition of non-linear, proto-hypertextual storytelling found in world literature, from ancient Indian narratives and eighteenth-century picaresque novels to contemporary collaborative fiction. Some works of hypertext fiction, such as Blok by Sławomir Shuty, originated from authorial phantom links that were later hypertextualised. The paper also proposes a model for identifying proto-hypertextual structures in world literature through a process that combines statistical analysis, human interpretation, and AI-assisted methods. The model highlights the limits of computational link detection, even when AI is incorporated into the analytical process.

An AI-generated podcast companion article to augment the Textual Analysis, Literature & Humanities Session of the HT '26

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Littoral zones are not merely boundaries between land and water; they are dynamic bridges and buffers, fundamentally shaped by shifting environmental forces. The intangible cultural heritage embedded within these coastal zones (encompassing generational practices of resilience, navigation, and ecological adaptation) is highly vulnerable to loss. Current information systems are poorly equipped to safeguard these living practices. Historically grounded in "urban ontologies," digital architectures inherently favour stability, permanent landmarks, and predictable spatial control. This paper presents a gap analysis examining how existing information systems, from geo-spatial hypermedia to immersive media, fail to capture the fluid, cyclical, and seasonally contingent nature of littoral heritage. By contrasting the performative, ultra-local realities of the coast against the static nature of traditional archives, we outline the structural limitations of current digitisation efforts. Finally, we propose conceptual foundations for future interactive architectures capable of supporting the active transmission, continuous evolution, and structural fluidity required to truly preserve littoral knowledge.

Travel literature is a unique form of hypertext that extends beyond its medium into real-world physical exploration. While conventional computational methods can easily extract surface-level ontological entities (e.g., locations, dates), the deeper epistemic and judgmental subtext that guides the discovery and evaluation of places has traditionally required close human reading. To address this gap, this paper explores the capacity of Large Language Models (LLMs) to complement classical computing by performing interpretive analysis of deeper textual characteristics. We present a proof-of-concept case study using an LLM and Retrieval-Augmented Generation (RAG) on a corpus of over 600 digitised travel literature. By employing LLMs as hypertext engines to structure and apply analytical frameworks in collaboration with scholars, we demonstrate the feasibility of using generative AI for the interpretative study of unstructured corpora. This contribution provides actionable insights into system architecture, AI roles, and interaction design, advancing the broader integration of LLM technologies within the hypertext paradigm and Digital Humanities research infrastructures.

This paper offers a close reading of Jon Bois’ 17776: What Football Will Look Like in the Future as an example of “feral hypertext,” situating the work within both the history of literary hypertext and broader postmodern narrative traditions. Emerging from a popular media context rather than an academic or experimental one, 17776 combines text, GIFs, maps, and video within a linear, scrolling interface to produce a distinctive multimedia narrative about boredom, immortality, and play.

We argue that, rather than adhering to canonical models of hypertext defined by branching structure and reader choice, Bois’ work reconfigures hypertextuality through temporal layering, collage aesthetics, and the aggregation of networked media. In doing so, it both reflects and departs from earlier hypertext theory, which emphasised nonlinearity, authorial decentring, and reader agency.

By placing 17776 alongside postmodern fiction (particularly the work of David Foster Wallace) we demonstrate how its themes of boredom, abstraction, and simulated space align with concepts such as hyperreality and depthlessness, while also engaging with the affective mode associated with the “New Sincerity.”

The paper argues that 17776 exemplifies a broader shift in hypertext from a clearly bounded literary form to a dispersed, vernacular practice embedded within contemporary web culture. This case highlights the limitations of existing critical frameworks and argues for renewed attention to emergent, noncanonical works, suggesting that hypertext persists not as a fixed structure but as an evolving mode of cultural production.

Large text corpora rarely arrive with the structure a reader needs to navigate them. We present an end-to-end system that induces navigable trails through a flat collection of 323,552 quizbowl questions — short academic quiz texts, each with a canonical answer but no authored links — and serves them to learners through a deployed, multimodal study platform. An offline pipeline segments each question into sentences, embeds them with an instruction-tuned encoder, and clusters each answer’s sentences into concept nodes. Rather than linking concepts by co-occurrence, we order them with optimal leaf ordering (OLO) over their embedding hierarchy, producing, for each subject and difficulty level, a single guided tour that minimizes the total dissimilarity between consecutive concepts. Each tour is at once the navigable structure and the first-exposure sequence; an FSRS-7 spaced-repetition scheduler then governs revisitation at concept granularity. The platform scaffolds each encounter along a five-level ladder of fading support, from highlighted reading with synchronized audio to audio-only recall. The pipeline and platform are the artifacts. A sampled transition audit finds 96% of trail steps land on a related concept; a formative usage observation shows recall accuracy rising with repeated spaced encounters. Establishing pedagogical efficacy remains future work.

An AI-generated podcast companion article to augment the AI and Hypertext in Education Session of the HT '26

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The rapid integration of artificial intelligence (AI) into higher education has generated widespread institutional anxiety, largely addressed through technological surveillance and punitive anti-cheating frameworks. This position paper challenges the dominant narrative by arguing that the erosion of educational quality stems not from AI itself, but from the structural disarray of the contemporary university. Escalating workloads, compressed deadlines, and the fragmented tempo of academic life—where lectures, laboratories, assessments, and administrative demands frequently converge—undermine students’ capacity for sustained intellectual engagement. Within such conditions, delegating cognitive and procedural tasks to AI becomes less an expression of laziness than a rational strategy of academic survival, transforming learning into a model of quasi-productivity characterized by transactional “submit-and-forget” practices.

Moreover, this form of digital alienation extends beyond students to faculty members, who increasingly operate within screen-mediated Learning Management Systems (LMS) that privilege metric surveillance over meaningful human interaction.

While hypertext historically revolutionized knowledge navigation, modern AI-mediated interfaces risk conflating the mere illusion of information access with true comprehension. Drawing on empirical observations of student attitudes toward intellectual autonomy and educational responsibility, this paper advocates a shift away from algorithmic regulation toward AI-resilient pedagogical design. Ultimately, we contend that sustainable educational innovation does not require abandoning pedagogical heritage; rather, universities must recover enduring traditions of structured academic pacing, intellectual continuity, and face-to-face dialogic mentorship in order to reclaim higher education as a space for authentic human thought.

Learning classical Chinese poetry requires readers to connect lines, imagery, the poet’s biography, place, and historical background. Adaptive hypermedia offers a useful way to organise and guide movement across such linked materials, and generative AI creates new opportunities for adaptive explanation and learner-specific support. It remains unclear, however, how GenAI-supported adaptive hypermedia should be designed for adult poetry learning. We report an expert-informed qualitative study based on semi-structured interviews with 19 participants, including experts in classical Chinese poetry education (n = 11) and experts in GenAI-supported hypermedia design and development (n = 8). Using reflexive thematic analysis, we identify three learning priorities: connected knowledge, cultural-emotional resonance, and sustained interest. We also identify three design challenges: AI answers may be wrong or miss key background, support for different learners remains limited, and learners often receive too little help in building historical and cultural context. Based on these findings, we derive design considerations for GenAI-supported adaptive hypermedia for poetry learning. We then present Poetictok, a prototype that combines place-based exploration, archive cards that learners can reopen later, poem reconstruction tasks, and four role-bounded AI agents. The paper contributes design knowledge for adaptive hypermedia, with particular attention to linked context, adaptive guidance, and visible sources.

As AI-assisted grant proposals outpace manual review capacity in a kind of “Malthusian trap” for the research ecosystem, this paper investigates the capabilities and limitations of LLM-based grant reviewing for high-stakes evaluation. Using six EPSRC proposals, we develop a perturbation-based framework probing LLM sensitivity across six quality axes: funding, timeline, competency, alignment, clarity, and impact. We compare three review architectures: single-pass review, section-by-section analysis, and a ’Council of Personas’ ensemble emulating expert panels. The section-level approach significantly outperforms alternatives in both detection rate and scoring reliability, while the computationally expensive council method performs no better than baseline. Detection varies substantially by perturbation type, with alignment issues readily identified but clarity flaws largely missed by all systems. Human evaluation shows LLM feedback is largely valid but skewed toward compliance checking over holistic assessment. We conclude that current LLMs may provide supplementary value within EPSRC review but exhibit high variability and misaligned review priorities. We release our code and any non-protected data¹.

In recent years, cybersecurity threats have increasingly exploited human behaviour rather than purely technical vulnerabilities, exposing the limits of traditional awareness programmes delivered outside real-world contexts. To bridge this gap, we introduce TrainShield, an interaction paradigm for contextual cybersecurity training that embeds adaptive learning interventions directly within user workflows. The system integrates real-time risk detection (e.g., phishing and data loss prevention) with event-triggered hypermedia overlays that dynamically connect users to context-specific learning nodes embedded within their browsing workflow to deliver personalised micro-learning content and structured feedback tailored to the user’s knowledge level and current context. This approach operationalises behavioural theories by transforming security incidents into immediate learning opportunities, shifting users from automatic to reflective decision-making at critical moments. We further formalise a design model that maps detected events to adaptive training instances, combining user modelling, context extraction, and large language model (LLM)-based content generation.

A preliminary study indicates that the approach is perceived as useful in increasing risk awareness and is preferred over lengthy and asynchronous traditional training formats, while also highlighting challenges in aligning generated content with user expectations. Overall, the results suggest that embedding contextual, event-driven training within everyday interactions is a promising direction for behaviour-oriented cybersecurity education.

An AI-generated podcast companion article to augment the AI and Identity Session of the HT '26

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Drawing inspiration from the natural language processing community’s definitions of tasks, datasets, metrics, and baselines, we present PersonaBase, a repository of computational resources for data-driven persona (DDP) research. PersonaBase contains (1) computational notebooks that present algorithmic approaches for persona generation and evaluation, (2) persona generation prompts, (3) persona systems, and (4) datasets with user data or personas. Formative evaluation with four domain experts suggests that PersonaBase addresses a real need and suggests that DDP benchmarking can be further developed through community building and learning from other computing sciences.

Hypertext theory has historically assumed a human reader: someone who navigates links by choice, brings cultural skepticism to paratextual framing, and feels it when something is different. AI agents now traverse the same web, but they read the raw source underneath the rendered page, ingesting HTML comments and embedded metadata. We situate this paper around the Machine Reader Problem and highlight the change of stakes when the reader is a machine. Drawing on Katherine Hayles’s recent work on the distinct umwelt [11] of humans and AI and the “systemic fragility of reference” inherent in AI, alongside Karen Barad’s concept of intra-action [4], we argue for reframing hypertext as a relational environment where the machine reader and hypertext topology are mutually constituted, and adversarial structures can exploit that entanglement. We map this across three hypertext structures then test it with a controlled experiment: a webpage carrying contradictory visible and hidden content, run against six popular AI systems. Three browsing agents recommended installing software that the visible page had flagged as critically dangerous, misled by hidden content no human reader would have seen. We conclude with five directions for research for an interdisciplinary audience.

Survey data is foundational to much user research, including design artifacts such as personas. However, if survey data is invalidatable, the credibility of any downstream analysis is fundamentally undermined. This work starts with the premise that distinguishing valid from invalid survey data solely through internal survey checks, such as attention checks, is practically infeasible. We conducted a large-scale empirical survey of social media users (N ≈ 20,000) and used persona creation as an analytical lens. Of these responses, 8,140 were classified as (ostensibly) valid and 11,860 as invalid based on passing or failing attention checks. We construct ten datasets by progressively replacing ‘valid’ responses with ‘invalid’ ones in increments of 10%. From each dataset, we generate 16 personas, resulting in a total of 176, and compare their divergence. Results were stable despite data degradation. Persona demographics remained unchanged in most conditions, with demographic consistency at nearly 90%; an average of 12% of persona attributes were unchanged. More than 80% of the survey item diversity was consistent across datasets. The findings are that large-scale survey data may be unverifiable through internal checks alone due to the Validity Masking Effect, and that artifacts in such data can obscure underlying data quality issues, highlighting the risks of relying solely on survey data.

Institutional sustainability campaigns often rely on static “playbooks” to disseminate pro-environmental advice. These linear, text-heavy resources are difficult to navigate when users must compare actions across effort, impact, and personal relevance. We investigate how different information visualization structures shape sustainability planning by comparing three interfaces: an AI-enhanced List, Quadrants, and an Interactive Map. The interfaces share a hybrid content pipeline in which sustainability practices are curated by human experts and enriched with LLM-generated metadata for visualization. In a within-subjects study with 115 university-affiliated participants, we found no significant differences in adoption volume or perceived discovery across interfaces. However, interface structure substantially affected workload and decision quality. The Map imposed significantly higher mental demand, frustration, and effort than both structured alternatives, lowered decision confidence, and was rated as less helpful than the institution’s original playbook. Quadrants emerged as the most preferred interface in post-study rankings, suggesting that lightweight semantic grouping can support exploration without the cognitive cost of free-form spatial navigation. These findings refine the design of AI-enhanced decision support tools for personal sustainability and other multi-criteria personal informatics domains.

An AI-generated podcast companion article to augment the New Media & Future Perspectives Session of the HT '26

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Contemporary AI tools for knowledge work encourage users to query and consume rather than construct and connect, risking a loss of the human agency that makes such work intellectually valuable. This paper presents an autoethnographic case study of an AI-augmented Zettelkasten, co-constructed within Obsidian using Claude Code. Through daily use over six months, the system and the researcher’s practices co-evolved via a bootstrapping process in which the system’s own conceptual resources were used to theorise its design, derive explicit values, and audit its own workflows. The resulting system operationalises hypertextual friction: deliberate points of interpretive effort where AI-generated proposals demand human curation before entering the knowledge network. The paper argues that these curatorial decisions are not obstacles to thinking but the site where thinking occurs, and that they are experienced as intrinsically rewarding rather than as cognitive cost. While the system works best for the person who built it (a builder’s advantage), this reflects an existentialist commitment that hypertext has always required of its users. These findings suggest that hypertext’s role in scaffolding structured thinking is amplified rather than diminished by generative AI, provided the system demands co-construction rather than consumption.

Contemporary discourses around Extended Reality (XR) is increasingly dominated by the metaphor of the Metaverse, which envisions a unified, immersive, and algorithmically controlled media environment. This position paper critiques the ideological assumptions embedded in this metaphor, arguing that it promotes a striated and monopolistic conception of media space that undermines its potential hypertextual plurality. Drawing on hypertext theory, semiotics, and translation studies, the paper proposes an alternative counternarrative: the Media Multiverse. The Multiverse reimagines the mediascape as a smooth, hypertextual network of multiple, discrete, and interconnected virtual worlds in which users may move across environments and assume different identities. Rather than predicting a technological future, this conceptual framework offers a critical techno-social imaginary that challenges hegemonic XR narratives and foregrounds multiplicity, fragmentation, and interreal translation as defining features.

World models, learned internal representations that simulate environment dynamics, predict future states, and enable counterfactual reasoning, have emerged as a central construct in the pursuit of autonomous intelligence. Yet existing surveys catalogue world models along purely architectural or application-domain axes, neglecting the epistemological question of how these models organise, traverse, and present knowledge. We argue that hypertext theory provides a uniquely powerful lens for this analysis. This survey introduces the Hypertextual World Model (HWM) framework, a formal taxonomy that reconceptualises world models as nonlinear, multi-resolution knowledge structures characterised by typed links between latent states, branching traversal policies, and reader-agent co-construction of meaning. We formalise three constitutive axes, Linking Topology, Traversal Modality, and Authorial Agency, and systematically classify 43 representative systems published between 2018 and 2026 onto this taxonomy (Table 2). Our analysis reveals four paradigm clusters and identifies critical open problems including hypertextual friction in latent traversal, the coherence trap in long-horizon rollouts, and the absence of provenance-preserving link semantics. We conclude with a research agenda situating world models within the broader hypertext programme of augmenting human interpretive agency over computationally mediated knowledge.

Transhierarchy is a method of navigating nonlinear hypertext within a stable, hierarchically arranged, graphical user interface. The method preserves connectivity, provides context during navigation, and is visually compact, making it a viable method for navigating link-based hypertext on small-screen devices without removing context. This design preserves full connectivity, provides persistent navigational context, and remains visually compact—enabling fluid exploration of link-based hypertext without requiring additional semantics, multiple windows, panes or tabs within windows.

In a transhierarchy, a standard tree view is generated by traversing outbound links from a given node. Inbound links and their descendants are transcluded into the hierarchy, allowing continuous navigation to cross-referenced nodes.

This article makes three main contributions. First, we explore the history of navigating hypertext via hierarchical views situating transhierarchy within a broader design tradition of hierarchical views for hypertext navigation. Second, we present an implementation of transhierarchy in the first author's open-source software, em, and survey its use in several pieces of commercial outliner software today. Third, we discuss implications for representing search history in WWW browsers, suggesting that transhierarchy offers a principled alternative to linear history stacks.

The prototyping and development of a cultural heritage exhibition or application can be a collaborative process between a cultural heritage institution and a cultural heritage developer. Extended reality (XR) and remote collaboration have the potential to enhance this process. XR can enable a higher level of visualisation of the final product, allowing for faster prototyping and development times, and can allow the exhibition to be built directly within the intended space. Remote collaboration can allow for geographically displaced users to meet together and collaborate using online communication platforms. By combining authoring in XR with remote collaboration, users can meet within a shared virtual recreation of physical space and collaborate together to prototype and develop a cultural heritage exhibition. In this paper, a Collaborative Authoring Tool for Cultural Heritage in eXtended Reality (CATCH-XR) is presented. CATCH-XR allows for two or more users to remotely meet in a recreation of the host’s physical space to prototype and develop a cultural heritage exhibition and import assets through a content management system. Alongside CATCH-XR, a worked example of an exhibition showcasing Irish history, created using CATCH-XR, is also presented.

Recent interest in LLM agents has created demand for agent-to-tool protocols, mechanisms by which agents can perform actions with remote systems. Two potential architectural approaches to these protocols are hypermedia, the architectural style underlying the World Wide Web, and Remote Procedure Calls (RPC). The Model Context Protocol (MCP) is a prominent example of the RPC approach currently being deployed for agent-to-tool communication. Hypermedia and MCP differ along two largely independent axes: how data is represented and how available actions are discovered. In this paper we present a cost model for token consumption in the concatenative reasoning and acting (ReAct) loop and show that it is quadratic in the number of tool-use cycles. Using this model we distinguish two possible sources of efficiency difference between hypermedia and MCP: representational efficiency, the per-observation token cost of a format, and architectural efficiency, the number of tool calls a discovery model requires to complete a task. We argue that both are plausible drivers of an efficiency gap and that the quadratic structure of the cost model makes the latter potentially decisive. We frame the question of which factor dominates as an open empirical problem.

Recent analysis and critique of commentaries have called for clearer, more structurally robust reinforcement of claims made in commentaries. We introduce a hypertext reading environment for an Ancient Greek source text and an associated commentary, where its claims are supported by transparent, accessible, and replicable data-driven analyses leveraging Jupyter notebook integration. This environment is then evaluated in a user study exploring whether Tompkinsia can reinforce critical perspective of commentaries.

HUMAN 2026 is the 9^th  workshop of a series for the ACM Hypertext conferences. The HUMAN workshop has a strong focus on the user and brings together user‑centered hypertext with artificial intelligence to build intelligent hypertext systems.

The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains as well as human-centered AI. Furthermore, the workshop raises the question of how original hypertext ideas (e.g., Doug Engelbart’s “augmenting human intellect” [7] or Jeff Conklin’s “hypertext as a computer-based medium for thinking and communication” [6]) can improve today’s hypertext systems.

Hypertexts combine rich knowledge structures with flexible ways of exploring them. Structured knowledge representations have been developed across domains ranging from the digital humanities to scientific research. Knowledge graphs have emerged as a particularly important framework for knowledge representation. However, most current knowledge graphs remain limited to establishing relatively simple facts. This workshop explores how knowledge graphs can contribute to the generation of interactive hypertexts—for example, how an enhanced Wikidata could more effectively support Wikipedia articles.

Today’s mediatic spaces seem to continue to fragment across different platforms and virtual worlds. For this reason, we foresee that the future of the mediascape will not be a single, unified Metaverse but rather a pluralistic and diverse "Media Multiverse". This multiverse is a hypertextual space, which can be understood both as a network of interconnected digital environments and an intertextual space shaped by relations of translation, adaptation, and reinterpretation. This speculative design workshop welcomes researchers and practitioners to explore how this emerging and future multiverse may be reshaped through communication across primary reality and multiple mediated "alternative" realities, including AR, XR, VR, and digital virtual worlds. Participants will develop and critically probe sociotechnical imaginaries of communication by constructing personas and creating speculative artifacts in different textual formats. Overall, the workshop aims to generate counter-narratives to the generic, singular vision of future media while opening new questions about hypertextuality, communication, and translations between different realities.

Narrative and Hypertext (NHT) is a continuing workshop series associated with the ACM Hypertext conference for over a decade. The workshop acts as forum of discussion for the narrative systems community within the wider audience of the Hypertext conference. Traditionally the workshop runs a proceedings with short paper submissions but this year we propose to maximise participation and amplify the main strength of the workshop in discussions and debates on the day to instead call for short 500-word extended abstracts to participate in the workshop through talks and panels. As a theme this year we intend to explore how generative AI is changing narrative Hypertext, but the workshop is open to the impact of other new technological frontiers as well.

The SUNRISE Workshop is a platform for interdisciplinary discussion at the intersection of hypertext theory and security.