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Researcher profile

Daniel Hienert

Daniel Hienert contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Human-Computer InteractionInformation Retrieval

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Published work

6 published item(s)

preprint2026arXiv

Escaping the Filter Bubble: Evaluating Electroencephalographic Theta Band Synchronization as Indicator for Selective Exposure in Online News Reading

Selective exposure to online news occurs when users favor information that confirms their beliefs, creating filter bubbles and limiting diverse perspectives. Interactive systems can counter this by recommending different perspectives, but to achieve this, they need a real-time metric for selective exposure. We present an experiment where we evaluate Electroencephalography (EEG) and eye tracking as indicators for selective exposure by using eye tracking to recognize which textual parts participants read and using EEG to quantify the magnitude of selective exposure. Participants read online news while we collected EEG and eye movements with their agreement towards the news. We show that the agreement with news correlates positively with the theta band power in the parietal area. Our results indicate that future interactive systems can sense selective exposure using EEG and eye tracking to propose a more balanced information diet. This work presents an integrated experimental setup that identifies selective exposure using gaze and EEG-based metrics.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

EyeLiveMetrics: Real-time Analysis of Online Reading with Eye Tracking

Existing eye tracking software have certain limitations, especially with respect to monitoring reading online: (1) Most eye tracking software record eye tracking data as raw coordinates and stimuli as screen images/videos, but without inherent links between both. Analysts must draw areas of interest (AOIs) on webpage text for more fine-grained reading analysis. (2) The computation and analysis of fixation and reading metrics are done after the experiment and thus cannot be used for live applications. We present EyeLiveMetrics, a browser plugin that automatically maps raw gaze coordinates to text in real time. The plugin instantly calculates, stores, and provides fixation, saccade, and reading measures on words and paragraphs so that gaze behavior can be analyzed immediately. We also discuss the results of a comparative evaluation. EyeLiveMetrics offers a flexible way to measure reading on the web - for research experiments and live applications.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MIRA: An LLM-Assisted Benchmark for Multi-Category Integrated Retrieval

Users increasingly expect modern search systems to offer a unified interface that seamlessly retrieves information from diverse data sources and formats. However, current information retrieval (IR) evaluation benchmarks have not kept pace with this development, primarily due to the lack of test collections that represent the diversity of contemporary search domains. We address this critical gap with MIRA, a novel benchmark based on a large-scale social science search platform. MIRA is designed for category-aware ranking across heterogeneous categories - Publications, Research Data, Variables, and Instruments & Tools - within a single, unified evaluation framework. The proposed collection is distinctive in several ways: (1) it is built upon real user queries, providing a more realistic basis for evaluation; (2) it covers scholarly items from four distinct categories, enabling multi-faceted evaluation; and (3) it leverages a Large Language Model to generate topic descriptions and narratives, as well as for relevance assessment with respect to these topics, substantially reducing the labor and cost of test collection generation. We release this resource to benefit the community by providing a foundational testbed for the research on multi-faceted, category-aware, integrated, or cross-category information retrieval.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SaL-Lightning Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search

The emerging research field Search as Learning investigates how the Web facilitates learning through modern information retrieval systems. SAL research requires significant amounts of data that capture both search behavior of users and their acquired knowledge in order to obtain conclusive insights or train supervised machine learning models. However, the creation of such datasets is costly and requires interdisciplinary efforts in order to design studies and capture a wide range of features. In this paper, we address this issue and introduce an extensive dataset based on a user study, in which $114$ participants were asked to learn about the formation of lightning and thunder. Participants' knowledge states were measured before and after Web search through multiple-choice questionnaires and essay-based free recall tasks. To enable future research in SAL-related tasks we recorded a plethora of features and person-related attributes. Besides the screen recordings, visited Web pages, and detailed browsing histories, a large number of behavioral features and resource features were monitored. We underline the usefulness of the dataset by describing three, already published, use cases.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search

With the rise of voice assistants and an increase in mobile search usage, natural language has become an important query language. So far, most of the current systems are not able to process these queries because of the vagueness and ambiguity in natural language. Users have adapted their query formulation to what they think the search engine is capable of, which adds to their cognitive burden. With our research, we contribute to the design of interactive search systems by investigating the genuine information need in a product search scenario. In a crowd-sourcing experiment, we collected 132 information needs in natural language. We examine the vagueness of the formulations and their match to retailer-generated content and user-generated product reviews. Our findings reveal high variance on the level of vagueness and the potential of user reviews as a source for supporting users with rather vague search intents.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Role of Word-Eye-Fixations for Query Term Prediction

Throughout the search process, the user's gaze on inspected SERPs and websites can reveal his or her search interests. Gaze behavior can be captured with eye tracking and described with word-eye-fixations. Word-eye-fixations contain the user's accumulated gaze fixation duration on each individual word of a web page. In this work, we analyze the role of word-eye-fixations for predicting query terms. We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms. We use a dataset of 50 search sessions obtained through a lab study in the social sciences domain. Using established machine learning models, we can predict query terms with comparably high accuracy, even with only little training data. Feature analysis shows that the categories Fixation, Query Relevance and Session Topic contain the most effective features for our task.

0 citations0 reviewsNo code linkedOpen access