Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Impacts of Hiding Friends’ Liked Content on User-Content Engagement across Newsfeed Channels

Social media platforms often distribute content through different newsfeed channels, most commonly, social networks, algorithmic recommendations and trending content. Prior literature has investigated each channel’s impact on user-content engagement. However, little is known about the relationships between these channels. We investigate the impacts of limiting content display from the social network channel on the quantity and diversity of user-engaged content across channels. We leverage a natural experiment, where a social media platform hides friends’ liked content from the social network channel, to identify the impacts. Results show that hiding friends’ liked content reduces the quantity of users’ content engagement on the entire platform. Across channels, users increase their engagement with trending content but decrease their engagement with algorithmic recommendations. Further, restricting exposure to friends’ liked content reduces the diversity of users’ content engagement. Our results highlight the intercorrelation of user-content engagement across newsfeed channels and provide insights for newsfeed designs