Relevance in Web search: between content, authority and popularity

Олейник, Антон

doi:10.1007/s11135-021-01125-7

Cited by 11 publications

(3 citation statements)

References 48 publications

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“…Those testifying to our queries are those producing online content, be it news websites, personal blogs and videos, or discussion forums. Consequently, they have an impact on the beliefs we hold about the matter at hand by means of the content they produce and make algorithmically recognizable (Gillespie, 2017: 65), in a sense generating a collaborative framework for ‘the validation of truth by public opinion’ (Oleinik, 2022: 186). ‘As such, googling resembles testimony because of our epistemic reliance on those who produce the content that gets presented to us when we perform a Google search’ (Narayanan and De Cremer, 2022: 3).…”

Section: Theoretical Scaffoldingmentioning

confidence: 99%

“…Notwithstanding this, the impact of the search results is mediated by and through Google's own mix of content-based and collaborative algorithms (Oleinik, 2022: 186; Rogers, 2018: 7), which play a directive role in ranking, structuring, and presenting the information to which we are exposed (Pan et al, 2007; Schultheiß et al, 2018). This is important for several reasons.…”

Section: Theoretical Scaffoldingmentioning

confidence: 99%

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Google, data voids, and the dynamics of the politics of exclusion

Norocel

Lewandowski

2023

Big Data & Society

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This study deploys a critical approach to big data analytics to gauge the tentative contours of data voids in Google searches that reflect extreme-right dynamics of exclusion in the aftermath of the 2015 humanitarian crisis in Europe. The study adds complexity to the analysis of data voids, expanding the framework of investigation outside the USA context by concentrating on Germany and Sweden. Building on previous big data analytics addressing the politics of exclusion, the study proposes a catalogue of queries concerning the issue of migration in both Germany and Sweden on a continuum from mainstream to extreme-right vocabularies. This catalogue of queries enables specific and localized queries to identify data voids. The results show that a search engine's reliance on source popularity may lead to extreme-right sources appearing in top positions. Furthermore, using platforms for user-generated content provides a way for localized queries to gain top positions.

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Section: Theoretical Scaffoldingmentioning

confidence: 99%

Section: Theoretical Scaffoldingmentioning

confidence: 99%

Google, data voids, and the dynamics of the politics of exclusion

Norocel

Lewandowski

2023

Big Data & Society

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“…Our work differs from such publications with much originality. However, recent papers by Oleinik (2022), Sinclair-Desgagné (2021), Franzosi (2021), Rawat and Sood (2021), Chen et al (2014) may be mentioned as being of interest to readers for increasing the relevance size of the bibliography toward connected domains.…”

Section: C) Feature Selectionmentioning

confidence: 99%

Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods

et al. 2022

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Highly cited papers are influenced by external factors that are not directly related to the document's intrinsic quality. In this study, 50 characteristics for measuring the performance of 68 highly cited papers, from the Journal of The American Medical Informatics Association indexed in Web of Science (WOS), from 2009 to 2019 were investigated. In the first step, a Pearson correlation analysis is performed to eliminate variables with zero or weak correlation with the target (“dependent”) variable (number of citations in WOS). Consequently, 32 variables are selected for the next step. By applying the Ridge technique, 13 features show a positive effect on the number of citations. Using three different algorithms, i.e., Ridge, Lasso, and Boruta, 6 factors appear to be the most relevant ones. The "Number of citations by international researchers", "Journal self-citations in citing documents”, and "Authors' self-citations in citing documents”, are recognized as the most important features by all three methods here used. The "First author's scientific age”, "Open-access paper”, and "Number of first author's citations in WOS" are identified as the important features of highly cited papers by only two methods, Ridge and Lasso. Notice that we use specific machine learning algorithms as feature selection methods (Ridge, Lasso, and Boruta) to identify the most important features of highly cited papers, tools that had not previously been used for this purpose. In conclusion, we re-emphasize the performance resulting from such algorithms. Moreover, we do not advise authors to seek to increase the citations of their articles by manipulating the identified performance features. Indeed, ethical rules regarding these characteristics must be strictly obeyed.

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Studying War Propaganda

Oleinik

2024

A Comparative Analysis of Political and Media Discourses About Russia’s Invasion of Ukraine

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Relevance in Web search: between content, authority and popularity

Cited by 11 publications

References 48 publications

Google, data voids, and the dynamics of the politics of exclusion

Google, data voids, and the dynamics of the politics of exclusion

Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods

Studying War Propaganda

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