The amount of information on the web is ever-increasing, so people

develop strategies to select important news and information and avoid cognitive

load. Thereby, people prefer selecting and reading information that is

consistent with their own. This phenomenon is explained by the psychological

concepts of “selective exposure” and “confirmation bias” which can negatively

affect personal decision-making and might lead to misjudgments.

In this project, we will combine knowledge from the research areas of

interactive information retrieval, knowledge discovery, and human-computer

interaction to investigate options for counteracting selective exposure on the

web. The overall goal is to support users in overcoming selective exposure

behavior and make suggestions to inform themselves holistically about a topic.

Our goals are the following: (1) Investigating how selective exposure manifests

itself in interaction, behavioral, eye-tracking, and physiological data. (2)

Based on this data, examining machine learning models for real-time detection

of selective exposure. (3) Exploring how user interface components should be

designed to make the users aware of selective exposure and motivate them to

consume news with different thematic aspects of the overall topic.

To this end, we first collect interaction, behavioral, eye-tracking, and

physiological data in two lab studies. In the first study, we look at

selective exposure during exploratory web search in a real-world setting. In

the second study, we focus primarily on the aspect of reading news in a

controlled environment to collect further physiological data accompanying the

reading of articles that either confirm or contradict one's opinion. Second, we

build a software pipeline to detect selective exposure based on behavioral data

automatically. This includes a component that automatically identifies which

aspects of an overall topic have been consumed by participants and which have

not. Third, we want to provide meaningful awareness components that make

selective exposure perceptible and point users to supplemental aspects.

Therefore, we will evaluate different interaction designs with users. In the

final step, we will combine all insights and developed components and conduct a

user study in which selective exposure behavior in real-world web sessions is

automatically detected and the participant is made aware of it.