Beliefs and biases in web search

By Ryen W. White. 2013

208 citations recorded by the source corpus.

Subfields: Epidemiology, Information Systems, Sociology and Political Science

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “It includes a variety of platforms —like for social media and networking (e.g., Facebook), question and answering (e.g., Quora), or collaboration (e.g., Wikipedia)—and purposes from ﬁnding information (White, 2013) to keeping in touch with friends (Lampe et al., 2008).”
Measuring personalization of web search (2013, background): “ note that personalization is not useful for all types of queries. A recent study investigated the inherent biases of search engines and their impact on the quality of information that reaches people [65] They show that the combined e˛ect of people’s preferences and the system’s inherent bias result”
Bias on the web (2018, background): “White 27 explored a good example of how cultural and cognitive biases affect Web search engines, showing that users tend to choose answers aligned with their existing beliefs.”
Investigating User Perception of Gender Bias in Image Search: The Role of Sexism (2018, background): “More recently, studies have been undertaken to understand the impact of users’ beliefs and unconscious biases on their search interactions and experiences [11, 18].”
The Limits of Abstract Evaluation Metrics: The Case of Hate Speech Detection (2017, background): “For instance, White [7] reports that when searching for medical information, users often settle on wrong answers that confirm their personal beliefs.”
Microsoft Academic Graph: When experts are not enough (2020, background): “Similar observations and potentially promising techniques to rectify biases are abundant in the literature (e.g., Joachims, Swaminathan, & Schnabel, 2017; Wang, Bendersky, Metzler, & Najork, 2016; White, 2013).”
Opportunities and challenges in search interaction (2018, background): “Hu-mans are affected by many factors impacting recorded activities 14 (for example, cognitive biases, behavioral biases, common misconceptions, and misinformation and rumor).”