Der Einsatz von Künstlicher Intelligenz und maschinellem Lernen verändern die Bedingungen für Mediaplanung nachhaltig. Das wirkt sich gerade auch auf das Daten basierte Programmatic Advertising aus.

Human Factors in Hypertext 2021 (HUMAN'21) is the 4th workshop in this series. It is sponsored by ACM SIGWEB and is associated to the ACM Conference on Hypertext and Social Media 2021, which took place between August 30th and September 2nd as a virtual event. HUMAN'21 took place on August 30th-the conference's workshop day. This year's workshop is held in a difficult time: the global Corona pandemic makes attending meetings or traveling risky for our health. It almost seems as a contradiction to the 2019 ACM Hypertext motto "tear down the wall". However, today's digital media enables us to communicate over distance or even attend larger meetings online. This also opens the question about the role of hypertext as a medium for communication-a perfect topic match for the HUMAN workshop series, which has a user-centric focus on hypertext. The user-centric view 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. As such, the HUMAN workshop also appreciates previous work on hypertext systems. In order to support this, we invite researchers to give demos of vintage hypertext systems. Last year we started with Norman Meyrowitz presenting Intermedia; this year Mark Anderson has been invited to give a demo of Microcosm.

Historically, there has been a tendency to consider hypertext as a type of system, perhaps characterised by provision of links or other structure to users. In this article, we consider hypertext as a method of inquiry, a way of viewing arbitrary systems. In this view, what are traditionally called “navigational hypertext systems” might be considered as information retrieval systems. This opens the hypertext field to various other types of systems that traditionally would not be considered as part of the field. The change of view enables a deeper fusion of human and machine. In particular, today's AI-based, intelligent systems open the demand of synthesising automation (on the machine's side) and augmentation (on the user's side). This article is not about researching AI systems; it is about extending the view of hypertext systems to synthesise augmentation and automation. We specifically apply this view to intelligent systems, asking the question about how hypertext can act as a common medium between human and machine, particularly for knowledge intensive tasks. We propose spatial hypertext as a medium that enables users to create cognitive maps. Along these lines, we provide examples from multiple projects and examine how these applications can be productive.

KI und maschinelles Lernen sind in aller Munde, gerade im Marketing – aber gilt das auch für den B2B-Bereich? Welche konkreten Einsatzmöglichkeiten gibt es?

Während wir hierzulande noch über die DSGVO, die ePrivacy-Verordnung und den Umgang mit Browsercookies diskutieren, erlangt die Datenökonomie andernorts, etwa in den USA oder China, eine völlig neue Qualität: die digitale Verwertung von Informationen aus unserer DNS.

Virtual Private Network (VPN) protocols provide means for establishing secure inter-network links. However, they do not provide anonymity. VPN providers can monitor both ends of the connection. On the other hand, Onion Routing offers very good anonymity properties but offers significantly less throughput than typical VPN setups. An interesting compromise is using several VPN servers connected in series (cascading VPN). This paper evaluates the throughput of two VPN protocols, WireGuard and OpenVPN, in a cascading environment.

Without any doubt, creating links and navigational trails is fundamental to hypertext. The Web is the widest spread representative among all systems in the history of hypertext, although its underlying core concept is kept simple. This is why the Web's widely used link functionality is naive, supporting only embedded, unary and unidirectional links. A strength of the Web is its extensibility, giving us the opportunity to augment the current functionality of links. We showcase a browser plugin, which enables users to create and share complex links over the existing Web. Furthermore, we discuss the CB-OHS Mother, its link model and how this relates to existing work. The implementation adopts latest standardization efforts and is an update to older attempts of enabling external link services for the Web.

Ziel des Customer Experience Management ist es, die Bindung des Kunden an den Anbieter zu verstärken. Sämtliche Bereiche der Kundenbeziehung sollen auf ein "positives Erlebnis" ausgerichtet werden. Dem Einsatz von künstlicher Intelligenz (KI) kommt dabei eine wichtige Rolle zu.

Auch wenn es viele eigentlich nicht mehr hören können: Künstliche Intelligenz und Maschinelles Lernen prägen nach wie vor die öffentliche Diskussion. Zweifelsohne ist darunter auch viel „Buzzword Bingo“ zu finden – man gewinnt ja den Eindruck, dass inzwischen jeder neue Toaster künstlich intelligent ist. Auf der anderen Seite wird das Thema oft als sehr abstrakt begriffen, „echte“ KI erscheint dann noch als wenig konkret und eher als vage „Zukunftsoption“. Jedoch lohnt hier ein tiefergehender Blick. Auch wenn es uns oft nicht bewusst ist: Gerade im Marketing ist KI bereits allgegenwärtig – jedenfalls dann, wenn wir dem allgemeinen Branchenverständnis folgen, wonach wir hierunter die Fähigkeit von Systemen verstehen, eigenständig auch komplexe Aktionen auszuführen und aus deren Ergebnissen „hinzuzulernen“. Ziel ist es, ähnlich wie beim menschlichen Lernen, ausgerichtet an den „gewonnenen Erkenntnissen“, die eigene Verfahrensweise sowie die erzielten Ergebnisse zu optimieren.

This blue sky paper presents the Thoughts Reflection Machine (TRM) which combines hypertext technologies and intelligent components. Using hypertext, the TRM provides means to its users to express or communicate their thoughts and ideas. Furthermore, the machine suggests relevant information that trigger users' creative thinking. The TRM is an approach towards a tight cooperation between human and machine supporting both in their specific tasks in which they are most excellent in: creative problem solving respective computation of huge data sets.

User behavior and the resulting behavioral data forms the basis of personalized feeds, recommendations and advertisements in social networks such as Facebook. These platforms are now required to provide users with their personal data. However, these dumps with chronological data in different files do not provide users insight in overarching themes and connections in their online behavior. In this paper, we discuss the development and preliminary evaluation of an exploratory interface for visual data exploration. First insights include that the less obvious, more associative and obscure connections are more interesting and relevant to the user than very close semantic or temporal connections.

Social networks such as Facebook are required to provide users with their personal data. However, these dumps do not provide users insight in overarching themes in their online behavior. In this poster, we discuss the development of Mother, a spatial hypertext system for visual data exploration. First insights include that the less obvious connections are more interesting and relevant to the user than very close semantic or temporal connections.

Social networks use several user interaction techniques for enabling and soliciting user responses, such as posts, likes and comments. Some of these triggers may lead to posts or comments that a user may regret at a later stage. In this article, we investigate how users may be supported in reflecting upon their past activities, making use of an exploratory spatial hypertext tool. We discuss how we transform raw Facebook data dumps into a graph-based structure and reflect upon design decisions. First results provide insights in users motivations for using such a tool and confirm that the approach helps them in discovering past activities that they perceive as outdated or even embarrassing.

Wesentlicher Bestandteil von KI ist die Fähigkeit, eigenständig zu lernen. Ähnlich wie in der Pädagogik gibt es verschiedene Lehrmethoden, um ein System zu trainieren. Daraus ergeben sich somit auch unterschiedliche Einsatzmöglichkeiten im Marketing.