Search engines often provide featured snippets, which are boxed and placed above other results with the aim of directly answering user queries. To learn about how users judge the credibility of such results and how they influence search outcomes, a controlled web-based user study (N = 96) was conducted. Using resources made available by scholars in the community, we study featured snippets in a medical context with participants being tasked with determining whether a named treatment is helpful for a specified medical condition both before and after viewing the search results. Experimental conditions varied the presence and credibility of featured snippets. Our findings indicate that participants tend to overestimate the credibility of information in featured snippets. Featured snippets are, moreover, shown to often change users’ opinion about a topic, especially if they are uncertain. Showing correct information inside featured snippets helped participants make more accurate decisions, whereas incorrect or contradicting information led to more harmful outcomes.

During everyday web browsing and search users reveal many pieces of private information to third parties. Even though people report being concerned about their privacy online, they often do not take steps to protect it. This is known as the 'privacy paradox' in the literature. In this work we study two well-known strategies based on theories from the behavioral sciences, nudging and boosting, which encourage users to browse in a way that their private data are less exposed. First, an online survey (N=127) tested the comprehensibility and efficacy of various facts (boosts), before the most effective of these were evaluated against 'nudge' interventions previously shown to be efficacious in lab-studies. A three week naturalistic study (N=68) using a browser extension revealed that both nudges and boosts improve browsing privacy, as approximated by different measures. Boosts are also shown to improve user knowledge about privacy in the short term, but the benefit weakens over time.

Loss of privacy and encounters with misinformation are two challenges individuals are likely to encounter in their search for information on the web. These challenges have potential negative impacts, especially in search domains such as health search. Existing information retrieval (IR) systems offer users little (if any) guidance as to how to reduce the likelihood of such negative impacts. The sum of these problems provides motivation for experiments to identify elements of existing IR environments that might provide low-cost options to the user for improved search outcomes.

From their impacts to potential threats, privacy and misinformation are a recurring top news story. Social media platforms (e.g. Facebook) and information retrieval (IR) systems (e.g. Google), are now in the public spotlight to address these issues. Our research investigates an approach, known as Nudging, applied to the domain of IR, as a potential means to minimize impacts and threats surrounding both matters. We perform our study in the space of health search for two reasons. First, encounters with misinformation in this space have potentially grave outcomes. Second, there are many potential threats to personal privacy as a result of the data collected during a search task. Adopting methods and a corpus from previous work as the foundation, our study asked users to determine the effectiveness of a treatment for 10 medical conditions. Users performed the tasks on 4 variants of a search engine results page (SERP) and a control, with 3 of the SERP's being a Nudge (re-ranking, filtering and a visual cue) intended to reduce impacts to privacy with minimal impact to search result quality. The aim of our work is to determine the Nudge that is least impactful to good decision making while simultaneously increasing privacy protection. We find privacy impacts are significantly reduced for the re-ranking and filtering strategies, with no significant impacts on quality of decision making.

Privacy concerns are becoming a dominant focus in search applications, thus there is a growing need to understand implications of efforts to address these concerns. Our research investigates a search system with privacy warning labels, an approach inspired by decision making research on food nutrition labels. This approach is designed to alert users to potential privacy threats in their search for information as one possible avenue to address privacy concerns. Our primary goal is to understand the extent to which attitudes towards privacy are linked to behaviors that protect privacy. In the present study, participants were given a set of fact-based decision tasks from the domain of health search. Participants were rotated through variations of search engine results pages (SERPs) including a SERP with a privacy warning light system. Lastly, participants completed a survey to capture attitudes towards privacy, behaviors to protect privacy, and other demographic information. In addition to the comparison of interactive search behaviors of a privacy warning SERP with a control SERP, we compared self-report privacy measures with interactive search behaviors. Participants reported strong concerns around privacy of health information while simultaneously placing high importance on the correctness of this information. Analysis of our interactive experiment and self-report privacy measures indicate that 1) choice of privacy-protective browsers has a significant link to privacy attitudes and privacy-protective behaviors in a SERP and 2) there are no significant links between reported concerns towards privacy and recorded behavior in an information retrieval system with warnings that enable users to protect their privacy.

The interplay between human biases and the underlying data collection and algorithmic methods to present users with relevant information in information retrieval (IR) systems have undesirable side effects, such as filter bubbles, censorship and developing beliefs in false information. Previous work in the areas of interactive information retrieval, document classification, behavioral economics and user profiling provide the foundation for our research. Using existing knowledge about human bias and profile data, we propose leveraging this information to raise awareness to users about their behavior in the frame of IR systems and inferences made. It is our goal to understand to what extent user behavior is changed. Our position is that education and awareness are much better approaches to address the ethical and human rights concerns when compared to regulatory measures and non-transparent changes to IR algorithms. It is believed the approach outlined below has the potential to dampen the effects of filter bubbles, reduce consumption of misleading and potentially hateful content, to broaden perspectives and protect the fundamental human right to freedom of expression.