i-com Band 19 (2020) Heft 3

Reflecting on Social Media Behavior by Structuring and Exploring Posts and Comments

Fri, 01 Jan 2021 00:00:00 GMT

Reflecting on Social Media Behavior by Structuring and Exploring Posts and Comments Herder, Eelco; Roßner, Daniel; Atzenbeck, Claus 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for using such a tool and confirm that the approach helps them in discovering past activities that they perceive as outdated or even embarrassing.

Examining Autocompletion as a Basic Concept for Interaction with Generative AI

Fri, 01 Jan 2021 00:00:00 GMT

Examining Autocompletion as a Basic Concept for Interaction with Generative AI Lehmann, Florian; Buschek, Daniel Autocompletion is an approach that extends and continues partial user input. We propose to interpret autocompletion as a basic interaction concept in human-AI interaction. We first describe the concept of autocompletion and dissect its user interface and interaction elements, using the well-established textual autocompletion in search engines as an example. We then highlight how these elements reoccur in other application domains, such as code completion, GUI sketching, and layouting. This comparison and transfer highlights an inherent role of such intelligent systems to extend and complete user input, in particular useful for designing interactions with and for generative AI. We reflect on and discuss our conceptual analysis of autocompletion to provide inspiration and a conceptual lens on current challenges in designing for human-AI interaction.

How to Handle Health-Related Small Imbalanced Data in Machine Learning?

Fri, 01 Jan 2021 00:00:00 GMT

How to Handle Health-Related Small Imbalanced Data in Machine Learning? Rauschenberger, Maria; Baeza-Yates, Ricardo When discussing interpretable machine learning results, researchers need to compare them and check for reliability, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in wrong prediction of cancer, incorrect assessment of the COVID-19 pandemic situation, or missing early screening of dyslexia. Often only small data exists for these complex interdisciplinary research projects. Hence, it is essential that this type of research understands different methodologies and mindsets such as the Design Science Methodology, Human-Centered Design or Data Science approaches to ensure interpretable and reliable results. Therefore, we present various recommendations and design considerations for experiments that help to avoid over-fitting and biased interpretation of results when having small imbalanced data related to health. We also present two very different use cases: early screening of dyslexia and event prediction in multiple sclerosis.

Intelligent Questionnaires Using Approximate Dynamic Programming

Fri, 01 Jan 2021 00:00:00 GMT

Intelligent Questionnaires Using Approximate Dynamic Programming Logé, Frédéric; Pennec, Erwan Le; Amadou-Boubacar, Habiboulaye Inefficient interaction such as long and/or repetitive questionnaires can be detrimental to user experience, which leads us to investigate the computation of an intelligent questionnaire for a prediction task. Given time and budget constraints (maximum q questions asked), this questionnaire will select adaptively the question sequence based on answers already given. Several use-cases with increased user and customer experience are given.

The problem is framed as a Markov Decision Process and solved numerically with approximate dynamic programming, exploiting the hierarchical and episodic structure of the problem. The approach, evaluated on toy models and classic supervised learning datasets, outperforms two baselines: a decision tree with budget constraint and a model with q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy.

This setting is quite flexible and can incorporate easily initial available data and grouped questions.