Experimental Measures of News Personalization in Google News

Abstract

Search engines and social media keep trace of profile-and behavioral-based distinct signals of their users, to provide them personalized and recommended content. Here, we focus on the level of web search personalization, to estimate the risk of trapping the user into so called Filter Bubbles. Our experimentation has been carried out on news, specifically investigating the Google News platform. Our results are in line with existing literature and call for further analyses on which kind of users are the target of specific recommendations by Google.

It Matters How You Google It? Using Agent-Based Testing to Assess the Impact of User Choices and Algorithmic Curation on Political Google Search Results

“…They show that prior suicide-harmful, suicide-preventative or suicide-unrelated search behaviour had no impact on the display of a helpline SERP feature. Regarding Google News, Haim et al (2018) show that untrained accounts received approximately the same news articles as accounts trained according to different types of news users (also see Cozza et al 2016; but some support for personalisation found by Le et al 2019). In general, these algorithm audits suggest that algorithmic curation in information search is, at most, limited.…”

Scaling up search engine audits: Practical insights for algorithm auditing

“…News, images and video search results have also been subject to virtual agent-based auditing. Cozza et al [ 13 ] found personalisation effects for the recommendation section of Google News, but not for the general news section. In line with this, Haim et al [ 14 ] found that only 2.5% of the overall sample of Google News results ( N = 1200) were exclusive to four constructed agents based on archetypical life standards and media usage.…”

“…One of the most studied platforms are web search engines -almost half of the auditing works reviewed by Bandy [5] were focused on Google alone -as a plethora of concerns have been raised about representation, biases, copyrights, transparency and discrepancies in their outputs. Research has analysed issues in areas such as elections [6][7][8][9][10][11], filter bubbles [12][13][14][15][16][17], personalised results [18,19], gender and race biases [20][21][22], health [23][24][25], source concentration [10,[26][27][28][29], misinformation [30], historical information [31,32] and dependency on user-generated content [33,34].…”

“…Upon landing, the bot updates internal counters of the extension and removes historical data as shown in Table 1. 13 The bot navigates to the next search engine main page according to the list downloaded on Step 2 (e.g. https:// bing.com), and sets the next element of the query term list (or the first element if the current query term is the last of the list) as the current query term.…”

The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertising

“…In recent years, concerns about the possible negative effects of personalised online services have led to efforts to quantify their degree of personalisation. For example, personalisation in web search as well as online news has been measured by using similarity ratios (Cozza et al, 2016; Dos Santos et al, 2020; Hannák et al, 2017; Krafft et al, 2019; Le et al, 2019; Puschmann, 2018; Salehi et al, 2015). Similarity ratios have also been applied to measuring targeted online advertising (Balebako et al, 2012; Guha et al, 2010).…”