2020
DOI: 10.1080/1369118x.2020.1764605
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Partisan search behavior and Google results in the 2018 U.S. midterm elections

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Cited by 41 publications
(36 citation statements)
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References 47 publications
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“…Over the past three decades, a vast body of research about search engines has emerged, with scholars increasingly referring to them as new meta media (Metaxa et al, 2019;Puschmann, 2019;Trielli & Diakopoulos, 2020). As has been argued, search engines play an increasingly important role in 'distributing and shaping knowledge' (Granka, 2010, p. 364).…”
Section: Search Engines As Meta Media: Algorithmic Bias Under Authoritarian Rulementioning
confidence: 99%
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“…Over the past three decades, a vast body of research about search engines has emerged, with scholars increasingly referring to them as new meta media (Metaxa et al, 2019;Puschmann, 2019;Trielli & Diakopoulos, 2020). As has been argued, search engines play an increasingly important role in 'distributing and shaping knowledge' (Granka, 2010, p. 364).…”
Section: Search Engines As Meta Media: Algorithmic Bias Under Authoritarian Rulementioning
confidence: 99%
“…In democratic contexts, they have been shown to have the power to influence public opinion (Dutton et al, 2017) and sway undecided voters (Epstein & Robertson, 2015). Extant research has investigated the distribution of partisan viewpoints in search results about candidates (Metaxa et al, 2019), the influence of candidate-controlled webpages on a candidate's coverage in search results (Puschmann, 2019) and the consequences of partisan user search behavior (Trielli & Diakopoulos, 2020). The findings and conclusions of these studies, however, can be barely generalized to nondemocratic contexts, where different political and legal conditions prevail (Paltemaa et al, 2020;Toepfl, 2020).…”
Section: Search Engines As Meta Media: Algorithmic Bias Under Authoritarian Rulementioning
confidence: 99%
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“…There are a number of studies that claim to be focused on filter bubbles. However, the majority of these works is not actually taking into account algorithmic personalization but, instead, it is inquiring ideological preference (echo chambers) with social sciences methods [9,10]. These approaches do not consider the fact that algorithmic personalization is a product of the interaction between users and platforms and it is essentially passive since users have extremely limited or no control of their personalization.…”
Section: 2mentioning
confidence: 99%
“…We performed a network analysis using Gephi [19] to better understand and visualize how the recommender system creates a filter bubble around users watching the same video the same day. Thanks to the Medialab's tool Table2net 9 we extracted a network file from the csv file. We created a bipartite network linking two types of nodes: users' pseudonyms and suggested video's ID.…”
Section: Network Analysismentioning
confidence: 99%