Explainable Cross-Topic Stance Detection for Search Results

One way to help users navigate debated topics online is to apply stance detection in web search. Automatically identifying whether search results are against, neutral, or in favor could facilitate diversification efforts and support interventions that aim to mitigate cognitive biases. To be truly useful in this context, however, stance...

Evaluating explainable social choice-based aggregation strategies for group recommendation

Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group...

Disentangling Fairness Perceptions in Algorithmic Decision-Making: The Effects of Explanations, Human Oversight, and Contestability

Recent research claims that information cues and system attributes of algorithmic decision-making processes affect decision subjects' fairness perceptions. However, little is still known about how these factors interact. This paper presents a user study (N = 267) investigating the individual and combined effects of explanations, human...

Viewpoint Diversity in Search Results

Adverse phenomena such as the search engine manipulation effect (SEME), where web search users change their attitude on a topic following whatever most highly-ranked search results promote, represent crucial challenges for research and industry. However, the current lack of automatic methods to comprehensively measure or increase viewpoint...

Investigating the Influence of Featured Snippets on User Attitudes

Featured snippets that attempt to satisfy users' information needs directly on top of the first search engine results page (SERP) have been shown to strongly impact users' post-search attitudes and beliefs. In the context of debated but scientifically answerable topics, recent research has demonstrated that users tend to trust featured...

Combine Statistical Thinking With Open Scientific Practice: A Protocol of a Bayesian Research Project

Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data and to answer concrete statistical questions, but also by educating them about alternative frameworks, such as Bayesian inference. In this article, we describe how we...

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine...

Comprehensive viewpoint representations for a deeper understanding of user interactions with debated topics

Research in the area of human information interaction (HII) typically represents viewpoints on debated topics in a binary fashion, as either against or in favor of a given topic (e.g., the feminist movement). This simple taxonomy, however, greatly reduces the latent richness of viewpoints and thereby limits the potential of research and...

The Effects of Crowd Worker Biases in Fact-Checking Tasks

Due to the increasing amount of information shared online every day, the need for sound and reliable ways of distinguishing between trustworthy and non-trustworthy information is as present as ever. One technique for performing fact-checking at scale is to employ human intelligence in the form of crowd workers. Although earlier work has...

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset ((Formula presented.) participants...

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewpoint diversity is not trivial to assess. In this paper we use existing and novel ranking fairness metrics to evaluate viewpoint diversity in search result rankings. We...

Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations

Recent research has shown that explanations serve as an important means to increase transparency in group recommendations while also increasing users' privacy concerns. However, it is currently unclear what personal and contextual factors affect users' privacy concerns about various types of personal information. This paper studies the effect...

Understanding How Algorithmic and Cognitive Biases in Web Search Affect User Attitudes on Debated Topics

Web search increasingly provides a platform for users to seek advice on important personal decisions [6] but may be biased in several different ways [1]. One result of such biases is the search engine manipulation effect (SEME): when a list of search results relates to a debated topic (e.g., veganism) and promotes documents pertaining to a...

This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias

During online information search, users tend to select search results that confirm previous beliefs and ignore competing possibilities. This systematic pattern in human behavior is known as confirmation bias. In this paper, we study the effect of obfuscation (i.e., hiding the result unless the user clicks on it) with warning labels and the...

A Checklist to Combat Cognitive Biases in Crowdsourcing

Recent research has demonstrated that cognitive biases such as the confirmation bias or the anchoring effect can negatively affect the quality of crowdsourced data. In practice, however, such biases go unnoticed unless specifically assessed or controlled for. Task requesters need to ensure that task workflow and design choices do not trigger...

Helping users discover perspectives: Enhancing opinion mining with joint topic models

Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives. This paper explores how opinion mining can be enhanced with joint topic modeling, to identify distinct perspectives within the topic, providing an informative overview from unstructured text. We...

This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics

In web search on debated topics, algorithmic and cognitive biases strongly influence how users consume and process information. Recent research has shown that this can lead to a search engine manipulation effect (SEME): when search result rankings are biased towards a particular viewpoint, users tend to adopt this favored viewpoint. To better...