Interpretable Transformation and Analysis of Timelines through Learning via Surprisability

The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medical data, historical records, and global statistics. However, conventional analysis techniques often struggle with challenges such as high dimensionality, complex distributions, and sparsity. These limitations hinder the ability to extract meaningful insights from complex temporal datasets, making it difficult to identify trending features, outliers, and anomalies effectively. Inspired by surprisability -- a cognitive science concept describing how humans instinctively focus on unexpected deviations - we propose Learning via Surprisability (LvS), a novel approach for transforming high-dimensional timeline data. LvS quantifies and prioritizes anomalies in time-series data by formalizing deviations from expected behavior. LvS bridges cognitive theories of attention with computational methods, enabling the detection of anomalies and shifts in a way that preserves critical context, offering a new lens for interpreting complex datasets. We demonstrate the usefulness of LvS on three high-dimensional timeline use cases: a time series of sensor data, a global dataset of mortality causes over multiple years, and a textual corpus containing over two centuries of State of the Union Addresses by U.S. presidents. Our results show that the LvS transformation enables efficient and interpretable identification of outliers, anomalies, and the most variable features along the timeline.

AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.

Domain-based Latent Personal Analysis and its use for impersonation detection in social media

Zipf's law defines an inverse proportion between a word's ranking in a given corpus and its frequency in it, roughly dividing the vocabulary to frequent (popular) words and infrequent ones. Here, we stipulate that within a domain an author's signature can be derived from, in loose terms, the author's missing popular words and frequently used infrequent-words. We devise a method, termed Latent Personal Analysis (LPA), for finding such domain-based personal signatures. LPA determines what words most contributed to the distance between a user's vocabulary from the domain's. We identify the most suitable distance metric for the method among several and construct a personal signature for authors. We validate the correctness and power of the signatures in identifying authors and utilize LPA to identify two types of impersonation in social media: (1) authors with sockpuppets (multiple) accounts; (2) front-user accounts, operated by several authors. We validate the algorithms and employ them over a large scale dataset obtained from a social media site with over 4000 accounts, and corroborate the results employing temporal rate analysis. LPA can be used to devise personal signatures in a wide range of scientific domains in which the constituents have a long-tail distribution of elements.