scholarly journals You Can’t See What You Can’t See: Experimental Evidence for How Much Relevant Information May Be Missed Due to Google’s Web Search Personalisation

2020 ◽  
Author(s):  
C Lai ◽  
Markus Luczak-Roesch
Keyword(s):  
Experimental Study ◽  
Public Sector ◽  
Web Search ◽  
Potential Problem ◽  
Knowledge Work ◽  
Relevant Information ◽  
Web Search Engine ◽  
Online Searches ◽  
Potential Impact ◽  
Web Research

© 2019, Springer Nature Switzerland AG. The influence of Web search personalisation on professional knowledge work is an understudied area. Here we investigate how public sector officials self-assess their dependency on the Google Web search engine, whether they are aware of the potential impact of algorithmic biases on their ability to retrieve all relevant information, and how much relevant information may actually be missed due to Web search personalisation. We find that the majority of participants in our experimental study are neither aware that there is a potential problem nor do they have a strategy to mitigate the risk of missing relevant information when performing online searches. Most significantly, we provide empirical evidence that up to$$20\%$$ of relevant information may be missed due to Web search personalisation. This work has significant implications for Web research by public sector professionals, who should be provided with training about the potential algorithmic biases that may affect their judgments and decision making, as well as clear guidelines how to minimise the risk of missing relevant information.

scholarly journals You Can’t See What You Can’t See: Experimental Evidence for How Much Relevant Information May Be Missed Due to Google’s Web Search Personalisation

2020 ◽  
Author(s):  
C Lai ◽  
Markus Luczak-Roesch
Keyword(s):  
Experimental Study ◽  
Public Sector ◽  
Web Search ◽  
Potential Problem ◽  
Knowledge Work ◽  
Relevant Information ◽  
Web Search Engine ◽  
Online Searches ◽  
Potential Impact ◽  
Web Research

© 2019, Springer Nature Switzerland AG. The influence of Web search personalisation on professional knowledge work is an understudied area. Here we investigate how public sector officials self-assess their dependency on the Google Web search engine, whether they are aware of the potential impact of algorithmic biases on their ability to retrieve all relevant information, and how much relevant information may actually be missed due to Web search personalisation. We find that the majority of participants in our experimental study are neither aware that there is a potential problem nor do they have a strategy to mitigate the risk of missing relevant information when performing online searches. Most significantly, we provide empirical evidence that up to$$20\%$$ of relevant information may be missed due to Web search personalisation. This work has significant implications for Web research by public sector professionals, who should be provided with training about the potential algorithmic biases that may affect their judgments and decision making, as well as clear guidelines how to minimise the risk of missing relevant information.

scholarly journals Semantic Web Search Engines : A Comparative Survey

2019 ◽  
pp. 107-115
Author(s):  
Anita Kumari ◽  
Jawahar Thakur
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Retrieval System ◽  
Relevant Information ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Web Search Engine ◽  
Comparative Survey

Search engines play important role in the success of the Web. Search engine helps the users to find the relevant information on the internet. Due to many problems in traditional search engines has led to the development of semantic web. Semantic web technologies are playing a crucial role in enhancing traditional search, as it work to create machines readable data and focus on metadata. However, it will not replace traditional search engines. In the environment of semantic web, search engine should be more useful and efficient for searching the relevant web information. It is a way to increase the accuracy of information retrieval system. This is possible because semantic web uses software agents; these agents collect the information, perform relevant transactions and interact with physical devices. This paper includes the survey on the prevalent Semantic Search Engines based on their advantages, working and disadvantages and presents a comparative study based on techniques, type of results, crawling, and indexing.

COVID-19 Related Web Search Behaviors: An Analytical Study of Google Trends-based Query Volumes in Indonesia (Preprint)

2020 ◽  
Author(s):  
Vita Widyasari ◽  
Karisma Trinanda Putra ◽  
Jiun-Yi Wang
Keyword(s):  
Web Search ◽  
Public Awareness ◽  
Relevant Information ◽  
Community Response ◽  
Google Trends ◽  
P Value ◽  
Basic Information ◽  
First Case ◽  
The Government ◽  
Economic Anxiety

BACKGROUND The volume of search keywords on Google can be used as a reference to an ongoing online trend during COVID-19 pandemic. OBJECTIVE This study was aimed to estimate the responsiveness and public awareness in early days of the COVID-19 outbreak in Indonesia using Google Trends relative search volumes (RSV). METHODS Sixty terms or keywords forming six topics included in the analysis were basic information, prevention, government policy, socio-economic, anxiety, and other issues related to COVID-19. All these keywords were checked for surveillance purposes between January 1 and May 4, 2020. The Python programming language was used for data mining from Google Trends databases. Correlation analysis was conducted to examine the correlations between the incidence of COVID-19 and the search terms. RESULTS Community response and awareness in the six topics were associated with the number of COVID-19 cases (r range between 0.570-0.825, P-value<.005). Before the first case announced in Indonesian, the prominent topics were basic information and other issues. One month after the first case, all topics experienced an increase in RSV. In the phase of outbreak, socio-economic and anxiety got much more attentions. CONCLUSIONS The government should consider to optimize the internet as a media for timely delivering most relevant information and dynamically respond massive queries, and improve health communications to increase public awareness and intention to prevent the disease.

scholarly journals What online searches tell us about public interest and potential impact on behaviour in response to Minimum Unit Pricing of alcohol in Scotland

Addiction ◽  
2021 ◽  
Author(s):  
David A. Leon ◽  
Elad Yom‐Tov ◽  
Anne M. Johnson ◽  
Mark Petticrew ◽  
Elizabeth Williamson ◽  
...  
Keyword(s):  
Public Interest ◽  
Online Searches ◽  
Potential Impact ◽  
Unit Pricing

When Knowledge Work and Analytical Technologies Collide: The Practices and Consequences of Black Boxing Algorithmic Technologies

2021 ◽  
pp. 000183922110167
Author(s):  
Callen Anthony
Keyword(s):  
Knowledge Workers ◽  
Deep Understanding ◽  
Knowledge Work ◽  
Ethnographic Study ◽  
Great Promise ◽  
Process Data ◽  
Investment Bank ◽  
Career Trajectories ◽  
Potential Impact ◽  
Analytical Tools

Analytical technologies that structure and process data hold great promise for organizations but also may pose fundamental challenges for how knowledge workers accomplish tasks. Knowledge workers are generally considered experts who develop deep understanding of their tools, but recent observations suggest that in some situations, they may black box their analytical technologies, meaning they trust their tools without understanding how they work. I conducted a two-year inductive ethnographic study of the use of analytical technologies across four groups in an investment bank and found two distinct paths that these groups used to validate financial analyses through what I call “validating practices”: actions that confirm whether a produced analysis is trustworthy. Surprisingly, engaging in these practices does not necessarily equate to understanding the calculations performed by the technologies. In one path, validating practices are partitioned across junior and senior roles: junior bankers engage in assembling tasks and use the analytical tools to perform analysis, while only senior bankers interpret the analysis. In the other path, junior and senior members engage in co-construction: junior bankers do both assembling and interpreting tasks, and senior bankers engage in interpreting and provide feedback on junior bankers’ reasoning and choices. Both junior and senior bankers in the partitioning groups routinely black boxed the algorithms embedded in their technologies, taking them for granted without understanding them. By contrast, bankers in the co-construction groups were conscious of the algorithms and understood their potential impact. I found that black boxing influenced the knowledge outputs of these bankers and constrained the development of junior members’ expertise, with consequences for their career trajectories.

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