We study how consumers express sustainability-related needs in online product search and how query autocompletion (QAC) systems mediate this intent. Using a 1% random sample (3.95 M queries) from the AmazonQAC dataset, we identify sustainable- and consumption-oriented vocabulary through a hybrid lexicon-based approach and analyse how QAC preserves, removes, or introduces such terms. Only about 1% of queries contain explicit sustainability intent, concentrated in categories like Food & Grocery and Health & Beauty. QAC preserves users’ sustainable intent in 60% of cases and adds sustainability-related tokens in a further 40%, indicating that it can reinforce rather than suppress ethical consumption cues. Regression analyses show that these additions occur more often in longer and more frequent queries. Our findings challenge the assumption that digital search infrastructures inherently bias users toward unsustainable consumption and highlight opportunities for QAC design to support responsible shopping behaviour.

Millions of consumers search for products online each day, aiming to find items that meet their needs at an acceptable price. While price and quality are major factors in purchasing decisions, ethical considerations increasingly influence consumer behavior -giving rise to the socially responsible consumer. Insights from a recent survey of over 600 consumers reveal that many barriers to ethical shopping stem from information-seeking challenges, often leading to decisions made under uncertainty. These challenges contribute to the intention-behaviour gap, where consumers' desire to make ethical choices is undermined by limited or inaccessible information and inefficacy of search systems in supporting responsible decision-making. In this perspectives paper, we argue that the field of Information Retrieval (IR) has a critical role to play by empowering consumers to make more informed and more responsible choices. We present three interrelated perspectives: (1) reframing ethical consumption as an information extraction problem aimed at reducing information asymmetries; (2) redefining product search as a complex task requiring interfaces that lower the cost and burden of responsible search; and (3) reimagining search as a process of knowledge calibration that helps consumers bridge gaps in awareness when making purchasing decisions. Taken together, these perspectives outline a path from query to conscience - one where IR systems help transform everyday product searches into opportunities for more ethical and informed choices. We advocate for the development of new and novel IR systems and interfaces that address the intricacies of socially responsible consumerism, and call on the IR community to build technologies that make ethical decisions more informed, convenient, and aligned with economic realities.

Work in Progress Share on Information Quality in Information Interaction and Retrieval: Workshop proposal for CHIIR 2022 Authors: Frans van der Sluis Department of Communication, University of Copehagen, Denmark Department of Communication, University of Copehagen, DenmarkView Profile , Catherine Smith Kent State University, United States Kent State University, United StatesView Profile , Toine Bogers Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , Florian Meier Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile Authors Info & Claims CHIIR '22: ACM SIGIR Conference on Human Information Interaction and RetrievalMarch 2022 Pages 371–373https://doi.org/10.1145/3498366.3505798Published:14 March 2022Publication History 0citation56DownloadsMetricsTotal Citations0Total Downloads56Last 12 Months56Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Social network analysis (SNA) is an empirical approach and a set of techniques that investigates actors, their dyadic links and the network they form. In this half-day tutorial, participants will learn about social network analysis as a tool for data analysis and visualization and how it can be applied in studies of information behaviour and interactive information retrieval. Participants will learn about its theoretical substantiation and gain practical experience by applying these theoretical concepts in a hands-on session using the open-source software Gephi.

In this study, we pair different perspectives on information sharing held by the information behaviour and computational social science communities. By reflecting on different conceptual models of sharing (two-actor vs three-actor model) and applying methods from social network analysis and text mining, we investigate the influence that different user interface features (retweeting and quote retweeting) have on politicians' sharing behaviour during an election campaign on the social media platform Twitter. Amongst other results, our analyses show that the two features are used quite differently with the quote RT feature promoting non-partisan interaction, which leads to a more civilized discourse with opponents and support for colleagues.

Returning to previously viewed or possessed information - re-finding - is a core information seeking behaviour that has been studied in diverse contexts including physical environments, personal computer filing systems, web search and email. Despite being designed for real-time and ephemeral content, recent studies have shown that re-finding of older content is performed in Social Media applications too. To better understand why this is and how re-finding can be better supported, in this work we describe the results of a large-scale web-based survey which queried 606 Twitter users on how and how often they re-find, as well as the motivations for this behaviour. Our main contribution is the qualitative analysis of these motivations and motivations sourced via two existing studies, resulting in a coding scheme documenting the breadth and frequency of different Social Media re-finding tasks. We discuss how this classification can be used in (i) the design of task-based evaluations, (ii) the detection and interpretation of re-finding in click-stream data and (iii) the design of Social Media search systems.

Social Media (SM) has become a valuable information source to many in diverse situations. In IR, research has focused on real-time aspects and as such little is known about how long SM content is of value to users, if and how often it is re-accessed, the strategies people employ to re-access and if difficulties are experienced while doing so. We present results from a 5 month-long naturalistic, log-based study of user interaction with Twitter, which suggest re-finding to be a regular activity and that Tweets can offer utility for longer than one might think. We shed light on re-finding strategies revealing that remembered people are used as a stepping stone to Tweets rather than searching for content directly. Bookmarking strategies reported in the literature are used infrequently as a means to re-access. Finally, we show that by using statistical modelling it is possible to predict if a Tweet has future utility and is likely to be re-found. Our findings have implications for the design of social media search systems and interfaces, in particular for Twitter, to better support users re-find previously seen content.