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Abstract
Images are characterized by a complex system of attributes. One of the most elusive properties of an image is its emotiveness – the ability to trigger an emotional reaction in the viewer. Prior research has demonstrated that users react to image emotiveness, yet to date there is little theoretical understanding and scarce empirical evidence as to the role played by emotiveness in the image retrieval process. Our study aims to fill this gap and investigates how users perceive the emotional content of the image-seeking task; explicate this emotional content in search keywords; and react to image emotiveness in selecting relevant images. Using a multimethod approach that combines quantitative and qualitative analyses, we performed three experiments where participants searched for images, both within the predefined set of images and in an open Web environment. Our findings suggest that although seekers rarely explicate image emotiveness in search keywords, their decision to select relevant images is largely dependent on their perception of the emotional content of the task and images. We discuss implications for research on emotions in image retrieval, as well as practical implications for designers of search systems.
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Additional information
Notes on contributors
Lev Poretski
Lev Poretski ([email protected], https://sites.hevra.haifa.ac.il/ylanir/) is a researcher with an interest in HCI; he is a student in the Department of Information Systems of University of Haifa, Abba Houshi blvd. 199, 3498838, Israel.
Joel Lanir
Joel Lanir ([email protected], http://hevra.haifa.ac.il/is-web/index.php?lang=en) is a Scientist with an interest in HCI; he is a professor in the Department of Information Systems of University of Haifa.
Ofer Arazy
Ofer Arazy ([email protected], [email protected]) is a Scientist with an interest in Collaboration in Online Communities; he is a professor in the Department of Information Systems of University of Haifa.