Web search has evolved into a platform people rely on for opinion formation on debated topics. Yet, pursuing this search intent can carry serious consequences for individuals and society and involves a high risk of biases. We argue that web search can and should empower users to form opinions responsibly and that the information retrieval community is uniquely positioned to lead interdisciplinary efforts to this end. Building on digital humanism---a perspective focused on shaping technology to align with human values and needs---and through an extensive interdisciplinary literature review, we identify challenges and research opportunities that focus on the searcher, search engine, and their complex interplay. We outline a research agenda that provides a foundation for research efforts toward addressing these challenges.@en

Featured snippets that attempt to satisfy users' information needs directly on top of the first search engine results page (SERP) have been shown to strongly impact users' post-search attitudes and beliefs. In the context of debated but scientifically answerable topics, recent research has demonstrated that users tend to trust featured snippets to such an extent that they may reverse their original beliefs based on what such a snippet suggests; even when erroneous information is featured. This paper examines the effect of featured snippets in more nuanced and complicated search scenarios concerning debated topics that have no ground truth and where diverse arguments in favor and against can legitimately be made. We report on a preregistered, online user study (N = 182) investigating how the stances and logics of evaluation (i.e., underlying reasons behind stances) expressed in featured snippets influence post-task attitudes and explanations of users without strong pre-search attitudes. We found that such users tend to not only change their attitudes on debated topics (e.g., school uniforms) following whatever stance a featured snippet expresses but also incorporate the featured snippet's logic of evaluation into their argumentation. Our findings imply that the content displayed in featured snippets may have large-scale undesired consequences for individuals, businesses, and society, and urgently call for researchers and practitioners to examine this issue further.

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HealthRecSys 2019 was the 4th International Workshop on Health Recommender Systems held in conjunction with the 2019 ACM Conference on Recommender Systems in Copenhagen, Denmark. This workshop followed on from of the previous workshop in 2018 [4] and focused on the application and potentials of recommender systems on health promotion, health care and health-related topics. By engaging the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure.

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The 3rd International Workshop on Health Recommender Systems was held in conjunction with the 2018 ACM Conference on Recommender Systems in Vancouver, Canada. Following the two prior workshops in 2016 [4] and 2017 [2], the focus of this workshop is to deepen the discussion on health promotion, health care as well as health related methods. This workshop also aims to strengthen the HealthRecSys community, to engage representatives of other health domains into cross-domain collaborations, and to exchange and share infrastructure.

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The 2017 Workshop on Health Recommender Systems was held in conjunction with the 2017 ACM Conference on Recommender Systems in Como, Italy. Following the fists workshop in 2016, the focus of this workshop was on enhancing the results of the first workshop by elaborating discussions on the topics, attracting scientist from other domains, finding cross-domain collaboration, and establishing shared infrastructures.

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The queries submitted by users to search engines often poorly describe their information needs and represent a potential bottleneck in the system. In this paper we investigate to what extent it is possible to aid users in learning how to formulate better queries by providing examples of high-quality queries interactively during a number of search sessions. By means of several controlled user studies we collect quantitative and qualitative evidence that shows: (1) study participants are able to identify and abstract qualities of queries that make them highly effective, (2) after seeing high-quality example queries participants are able to themselves create queries that are highly effective, and, (3) those queries look similar to expert queries as defined in the literature. We conclude by discussing what the findings mean in the context of the design of interactive search systems. @en