Welcome to the Human Factors in Hypertext 2018 workshop (HUMAN'18) in Baltimore, Maryland, USA, the first workshop of a new series of workshops for the ACM Conference on Hypertext and Social Media. It has a strong focus on hypertext users and thus complements the machine analytics research that we experienced in previous conferences. The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains. Furthermore, the workshop raises the question of how original hypertext ideas (e.g., Doug Engelbart's "augmenting human intellect" or Frank Halasz' "hypertext as a medium for thinking and communication") can improve today's hypertext systems. Historically, hypertext research is strongly connected to human factors. Hypertext pioneers, such as Doug Engelbart or Ted Nelson, focused on the usage of and interaction with hypertext. This workshop combines original hypertext research ideas with recent hypertext research trends. In addition, it consolidates different hypertext research areas from the viewpoint of human factors. Thus, HUMAN'18 fosters cross-cutting discussions and the development of new ideas.

The idea to associate information with so-called links was developed by hypertext pioneers in the 1960s. In the 1990s the Dexter Hypertext Reference Model was developed with the goal to provide a general model for node-link hypertext systems. In the 1990s and 2000s there were important steps made for hypertext infrastructures, which led to component-based open hypermedia systems (CB-OHS). In this paper we provide a detailed description of node-link structures. We argue that Dexter does not match the need of CB-OHS, as it supports a mix of multiple structure domains. Based on the implementation of link support in our system Mother we demonstrate how Dexter needs to be tailored accordingly. We further describe Mother's ability of node-link structures to interoperate with other available structure services and vice versa.

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Software applications become more and more dominant in our daily life and work. However, it is very difficult to provide sophisticated tools for all arising use cases. End-User Development (EUD) is a term that describes the development of applications by end users rather than professional developers. This enables them creating highly specialized solutions. End users are laypersons when it comes to building software, therefore they need appropriate tools for managing the whole development process. This includes designing, implementing and deploying applications. While there are already various tools available, we focus on a recommendation feature for graphical EUD tools, utilizing their spatial hypertext capabilities. In this paper we provide an overview of some common issues such tools are often struggling with. We explain how visually analyzing the workspace, parsing an implicit spatial hypertext and eventually presenting recommendations may tackle them. We further describe the project HEIMDALL in detail, especially the way of generating recommendations for software modules with the aim to raise users' awareness. Furthermore, we discuss the use of such a system that reaches a similar understanding of relationships between software modules as users have. Finally, we point to open issues that still need to be addressed to improve results and their presentation.

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Specialized systems aiming at offering hypertext functionality in users' computing have been discussed since the early days of hypertext. However, with the claim to also support other structure domains than node-link structures, hypertext systems had to overcome some challenges. Researchers came up with component-based approaches and low level structure services.
Due to the raising omnipresence of the Web, research on traditional hypertext systems has been fading out over the past decade. This paper focuses again on hypertext infrastructures and goes beyond ongoing Web discussions. Based on lessons learned from well thought through previous work, we present a novel design for multi-structure supporting, general purpose hypertext systems that can be used in a series of application domains. The system provides intelligence analysis which is needed for sophisticated user support. We argue that this lets us use the hypertext system also as a visual analytics tool. Furthermore, for demonstration purposes we describe the use of the system in combination with a Web-based software engineering platform, which is part of the ongoing project ODIN.

Spatial Hypertext represents associations between chunks of information by spatial or visual attributes (such as proximity, color, shape etc.). This allows expressing information structures implicitly and in an intuitive way. However, automatic recognition of such informal, implicitly encoded structures by a machine (a so-called spatial parser) is still a challenge. One reason is, that conventional (non-adaptive) parsers are conceptually restricted by their underlying source of information (i. e., the spatial hypertext). Due to this limitation there are several types of structures that cannot be recognized properly. This inevitably limits both quality of parser output and parser performance. We claim that considering temporal aspects in addition to spatial and visual properties in spatial parser design will lead to significant increase in parsing accuracy, detection of richer structures and thus higher parser performance.

For the purpose of providing evidence, parsers for recognizing spatial, visual and temporal object relations have been implemented and tested in a series of user surveys. It turned out, that in none of the test cases pure spatial or visual parser could outperform the spatio-temporal parser. Instead, the spatiotemporal parser was able to compensate limitations of conventional parsers. Furthermore, differences in parsing accuracy were successfully tested for statistical significance. The results indicate a non-trivial effect that is recognizable by humans. We have shown that the addition of a temporal parser shifts machine detected structures significantly closer to what knowledge workers intend to express.