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This chapter mainly investigates the role of Artificial Intelligence (AI) in augmenting search interactions to enhance users’ understanding across various domains. The chapter begins by examining the current limitations of traditional search interfaces in meeting diverse user needs and cognitive capacities. It then discusses how AI-driven enhancements can revolutionize search experiences by providing tailored, contextually relevant information and facilitating intuitive interactions. Through case studies and empirical analysis, the effectiveness of AI-supported search interaction in improving users’ understanding is evaluated in different scenarios. This chapter contributes to the literature on AI and human–computer interaction by highlighting the transformative potential of AI in optimizing search experiences for users, leading to enhanced comprehension and decision-making. It concludes with implications for research and practice, emphasizing the importance of human-centered design principles in developing AI-driven search systems.
Storytelling is everyday information behavior that, when it goes wrong, can propagate misinformation. From accurate data to misinformed stories, what goes wrong with the process? This chapter focuses on the dynamics of storytelling in misinformation as a problematic aspect of the COVID-19 pandemic in three widely circulated problematic stories. Storytelling offers a framework for researching collective experiences of information as a process that is inherently based in communities, with knowledge commons that are instantiated by the telling and retelling of stories, temporarily or permanently. To understand how difficult information is to govern in story form and through storytelling dynamics, this chapter uses storytelling theory to explore three recent cases of COVID-19 misinformation related to medicine misuse, exploiting vaccine hesitancy, and aftermath of medical racism. Understanding what goes wrong with these stories may be key to public health communications that engage effectively with communitiesÕ everyday misinformation challenges.Ê
This article shows that citizens consider policy positions for the formation of their political preferences when they actively seek and find high-quality information, while they dismiss passively acquired and low-quality information. The study develops an extended theory of information and political preferences that incorporates the process of information acquisition and its connection with information quality. A novel experimental design separates the effects on political preferences due to information behavior as an activity from those due to selective exposure to information. The study applies this design in a laboratory experiment with a diverse group of participants using the example of issue voting and European integration in the context of the 2014 European Parliament elections.
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