Investigating the Effects of Emotions in Search Engine Featured Snippets

The process of web searching is influenced by various biases (Azzopardi 2021). One significant bias arises from the way search results are formulated and presented, which affects both user interactions with the results (Draws 2021) and the outcomes of their search sessions (Epstein 2015). Another factor contributing to bias is the presence of featured snippets, which are search results designed to directly answer users' queries. These snippets tend to capture more attention, shorten search time, and enhance user satisfaction with the search process and its outcomes (Wu 2020). These biases are heavily influencing the main goal of searching. The „search-as-learning“ research community has argued that learning is a important outcome of searching (Urgo 2022}. The learning effect can be influenced and we know from existing literature that emotions have a significant impact on our learning behaviour and on the ability to reflect information (Lerner 2015). Text comprehension is influenced by them, but also the retrieval of information from the text (Pekrun 2022). Although emotions have been shown to have an influence on knowledge gain, we do not know whether this is also the case with featured snippets. The question is whether featured snippets are suitable for increasing their learning effect through the targeted addition of emotions. In this context, it is important to consider the already mentioned strong influence of featured snippets on the recipients' opinion and argumentation logic, as emotions could further strengthen these effects.

The aim of this research is to find out whether sentiment as an overarching theme for emotions embedded in search engines' featured snippets has an impact on user engagement with debated topics. Specifically, different sentiment categories are assessed and compared with each other in terms of factual knowledge gain (measured by pre/post comparisons). Furthermore, interaction patterns are observed and the featured snippet's stance is also taken into account as a secondary influencing factor. If a significant correlation can be found between sentiment and knowledge gain, this could have an influence on the design of featured snippets with the aim of increasing the knowledge gain of users. On the other hand, this could also further bias the influence of the system and users' cognitive biases.

1. Define research questions and hypotheses
2. Conduct a literature review to ground the research in
3. Conduct pre-study to determine the existens of emotion and sentiment in featured snippets
4. Conduct controlled study
5. Analyze and interpret the data gathered in the study

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• Azzopardi, L. (2021). Cognitive biases in search: A review and reflection of cognitive biases in information retrieval.
• Draws, T. (2021). Comprehensive Viewpoint Representations for a Deeper Understanding of User Interactions With Debated Topics.
• Epstein, R.(2015). The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections.
• Lerner, S. (2015). Emotion and Decision Making.
• Pekrun, R. (2022). Emotions in Reading and Learning from Texts: Progress and Open Problems.
• Urgo, K. (2022). Learning assessments in search-as-learning: A survey of prior work and opportunities for future research.