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1. A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results

   https://doi.org/10.1145/3449078  

   Vincent, Nicholas; Hecht, Brent (April 2021, Proceedings of the ACM on Human-Computer Interaction)

   A growing body of work has highlighted the important role that Wikipedia's volunteer-created content plays in helping search engines achieve their core goal of addressing the information needs of hundreds of millions of people. In this paper, we report the results of an investigation into the incidence of Wikipedia links in search engine results pages (SERPs). Our results extend prior work by considering three U.S. search engines, simulating both mobile and desktop devices, and using a spatial analysis approach designed to study modern SERPs that are no longer just "ten blue links". We find that Wikipedia links are extremely common in important search contexts, appearing in 67-84% of desktop SERPs for common and trending queries, but less often for medical queries. Furthermore, we observe that Wikipedia links often appear in "Knowledge Panel" SERP elements and are in positions visible to users without scrolling, although Wikipedia appears less often and in less prominent positions on mobile devices. Our findings reinforce the complementary notions that (1) Wikipedia content and research has major impact outside of the Wikipedia domain and (2) powerful technologies like search engines are highly reliant on free content created by volunteers. 

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2. Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics

   https://doi.org/10.1145/3664190.3672520  

   Wang, Ben; Liu, Jiqun (August 2024, ACM)

   When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude changes of users searching online, our study examined how cognitively biased users interact with algorithmically biased search engine result pages (SERPs). We designed three-query search sessions on debated topics under various bias conditions. We recruited 1,321 crowdsourcing participants and explored their attitude changes, search interactions, and the effects of confirmation bias. Three key findings emerged: 1) most attitude changes occur in the initial query of a search session; 2) Confirmation bias and result presentation on SERPs affect the number and depth of clicks in the current query and perceived familiarity with clicked results in subsequent queries; 3) The bias position also affects attitude changes of users with lower perceived openness to conflicting opinions. Our study goes beyond traditional simulation-based evaluation settings and simulated rational users, sheds light on the mixed effects of human biases and algorithmic biases in information retrieval tasks on debated topics, and can inform the design of bias-aware user models, human-centered bias mitigation techniques, and socially responsible intelligent IR systems. 

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3. Classification of Health-Related Social Media Posts: Evaluation of Post Content–Classifier Models and Analysis of User Demographics

   https://doi.org/10.2196/14952  

   Rivas, Ryan; Sadah, Shouq A; Guo, Yuhang; Hristidis, Vagelis (January 2020, JMIR Public Health and Surveillance)

   Background The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. Objective The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. Methods We analyzed two different types of health-related social media: (1) health-related online forums—WebMD and DailyStrength—and (2) general online social networks—Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. Results We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users’ WebMD posts were more likely to ask for medical advice than female users’ posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. Conclusions We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message. 

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4. Fairness via AI: Bias Reduction in Medical Information

   Dori-Hacohen, S.; Montenegro, R.; Murai, F.; Hale, S. A.; Sung, K.; Blain, M.; Edwards-Johnson, J. (January 2021, The 4th FAccTRec Workshop on Responsible Recommendation at RecSys 2021)

   Most Fairness in AI research focuses on exposing biases in AI systems. A broader lens on fairness reveals that AI can serve a greater aspiration: rooting out societal inequities from their source. Specifically, we focus on inequities in health information, and aim to reduce bias in that domain using AI. The AI algorithms under the hood of search engines and social media, many of which are based on recommender systems, have an outsized impact on the quality of medical and health information online. Therefore, embedding bias detection and reduction into these recommender systems serving up medical and health content online could have an outsized positive impact on patient outcomes and wellbeing. In this position paper, we offer the following contributions: (1) we propose a novel framework of Fairness via AI, inspired by insights from medical education, sociology and antiracism; (2) we define a new term, bisinformation, which is related to, but distinct from, misinformation, and encourage researchers to study it; (3) we propose using AI to study, detect and mitigate biased, harmful, and/or false health information that disproportionately hurts minority groups in society; and (4) we suggest several pillars and pose several open problems in order to seed inquiry inthis new space. While part (3) of this work specifically focuses on the health domain, the fundamental computer science advances and contributions stemming from research efforts in bias reduction and Fairness via AI have broad implications in all areas of society. 

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5. Fairness via AI: Bias Reduction in Medical Information

   Dori-Hacohen, S.; Montenegro, R.; Murai, F.; Hale, S. A.; Sung, K.; Blain, M.; Edwards-Johnson, J. (January 2021, The 4th FAccTRec Workshop on Responsible Recommendation at RecSys 2021)

   Most Fairness in AI research focuses on exposing biases in AI systems. A broader lens on fairness reveals that AI can serve a greater aspiration: rooting out societal inequities from their source. Specifically, we focus on inequities in health information, and aim to reduce bias in that domain using AI. The AI algorithms under the hood of search engines and social media, many of which are based on recommender systems, have an outsized impact on the quality of medical and health information online. Therefore, embedding bias detection and reduction into these recommender systems serving up medical and health content online could have an outsized positive impact on patient outcomes and wellbeing. In this position paper, we offer the following contributions: (1) we propose a novel framework of Fairness via AI, inspired by insights from medical education, sociology and antiracism; (2) we define a new term, bisinformation, which is related to, but distinct from, misinformation, and encourage researchers to study it; (3) we propose using AI to study, detect and mitigate biased, harmful, and/or false health information that disproportionately hurts minority groups in society; and (4) we suggest several pillars and pose several open problems in order to seed inquiry inthis new space. While part (3) of this work specifically focuses on the health domain, the fundamental computer science advances and contributions stemming from research efforts in bias reduction and Fairness via AI have broad implications in all areas of society. 

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