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REVIEW 3 major objections 5 minor 74 references

Auditing News Curation Systems: A Case Study Examining Algorithmic and Editorial Logic in Apple News

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Apple News's algorithmic Trending Stories show no personalization or localization and concentrate on fewer and softer-news sources than the human-curated Top Stories.

desk verdict A worthwhile audit of Apple News with a genuine simulator-equivalence caveat that deserves peer review but needs tighter validation. read the letter →

arxiv 1908.00456 v1 pith:WPNE6OLW submitted 2019-08-01 cs.CY cs.HC

Jack Bandy , Nicholas Diakopoulos This is my paper · ORCID
classification cs.CYcs.HC
keywords newscurationsectionstoriesapplealgorithmicauditcontent
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Apple News is a news app on iPhones. It has two prominent sections: Top Stories, chosen by human editors, and Trending Stories, chosen by an algorithm. This study audited both sections to learn how they work and what they show. The researchers used three methods: crowdsourced screenshots from real users, automated 'sock puppet' simulators, and a two-month continuous scrape.

The personalization test asked dozens of workers to screenshot the app at the same times. Nearly every screenshot showed the same headlines as a control device, and no story appeared for one user only. A simulated-location test found no differences across all 50 US state capitals. So Trending Stories appear to be a single national list for everyone. Over two months, the algorithmically selected stories came from a more concentrated set of sources: the top three sources made up 45% of all stories, versus 24% for the human-edited section. The algorithm also featured more celebrity and entertainment stories, while the editors chose more policy and international news.

The paper is an example of algorithm auditing, treating a proprietary platform as a black box and observing it from the outside. It also offers a reusable framework and open-source code for future audits of similar news curators.

Extended reading notes

Core claim

Results showed that the human-curated Top Stories section features fewer stories per day and exhibits greater source diversity and greater source evenness compared to the algorithmically-curated Trending Stories section.

Load-bearing premise

The audit assumes that the Appium-controlled iPhone simulator presents the same content that real iPhones would show in the United States. If Apple serves different content to simulators (for example, test modes, different device identifiers, or time-dependent A/B variants), the findings on personalization, localization, and content distributions would not generalize to actual users. This assumption enters in the 'Sock Puppet Auditing via Appium' section and is used for the extended data collection in Experiment 4, which collected all content from a single simulated device.

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Editorial analysis

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Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents an audit of Apple News, focusing on the algorithmically curated Trending Stories section and the human-editorially curated Top Stories section. It introduces a three-part audit framework (mechanism, content, consumption), then applies it through four experiments: update frequency of Trending Stories, a GPS-based localization test using Appium simulators, a crowdsourced personalization test with synchronized Mechanical Turk screenshots, and a 62-day automated collection from a single simulator comparing the two sections. The headline findings are that Trending Stories show no meaningful personalization or localization in the tested settings, that the algorithmic section has higher source concentration and lower Shannon equitability than the editorial section, and that the algorithmic section skews toward soft news while the editorial section skews toward policy and international news. The paper interprets these differences as manifestations of algorithmic versus editorial logic.

Significance. If the findings hold, this is a valuable early empirical characterization of Apple News, a platform with substantial gatekeeping power but little public data. The study is methodologically constructive: the code is released, the measurements are computed directly from observed data without fitted parameters, and the structure of the personalization experiment (synchronized crowd screenshots compared with a control simulator) is a useful template for auditing closed platforms. The diversity comparison is supported by an appropriate significance test (Hutcheson's t, t=11.17, p<0.001). The main limitation is that the extended content audit rests on a single simulated device with only limited real-device validation, so the generalizability of the central source-diversity claim is not yet fully established.

major comments (3)
  1. [Experiment 4 / Results: Content / Table 2] The central content comparison (Table 2, Figures 3–4) is based on a single Appium-controlled simulator over 62 days, but the only real-device validation reported is Experiment 3, which covers Trending Stories at three synchronized time points and does not validate Top Stories or the full collection period. If the simulator receives different content than production iPhones (due to device/profile signals, IP-based location, or A/B variants), the source diversity and evenness differences could be artifacts of the measurement channel. I ask the authors to add validation (for example, periodic real-device screenshots for both sections during the 62-day window) or to explicitly constrain the claims to what the single-device data can support.
  2. [Experiment 2: Testing for location-based adaptation] The localization test manipulates only the simulator's GPS coordinates; the manuscript does not report varying the host IP address or Apple ID across conditions. Because Apple News may infer location from IP or account information rather than GPS, the null result does not rule out location-based adaptation. The paper should soften 'no evidence of localization' to 'no evidence of GPS-based localization' and discuss this residual confound in the limitations.
  3. [Discussion: Algorithmic vs. Editorial Logic] The comparison is observational: Top Stories and Trending Stories differ not only in human versus algorithmic curation but also in section purpose (top stories versus trending content), number of slots, and update frequency. The abstract and conclusion attribute the diversity difference to curation type ('human curation outperformed algorithmic curation'), but the design does not isolate curation logic from these other factors. I recommend adding an explicit caveat that the observed differences are consistent with, but not uniquely caused by, the editorial-versus-algorithmic distinction.
minor comments (5)
  1. [Table 2] The text reports 1,268 Top Stories collected, while Table 2 lists 1,267; the discrepancy should be reconciled.
  2. [Table 1] The row 'meghan markle 33*' in the Trending Stories n-gram table is confusing: the asterisk footnote says the n-gram appeared twice in the other section, but the row is placed among Trending n-grams. Please clarify the intended placement and annotation.
  3. [Abstract] The claim of being 'the first data-backed characterization of Apple News in the United States' is strong; given prior work by Brown (2018a, 2018b) on Apple News, consider softening the novelty claim or specifying more precisely what aspect is new.
  4. [Experiment 1] The distinction between platform-wide and user-specific update frequency is clear in principle, but the finding that the app must be closed to see updates is important and should be highlighted explicitly in the main results rather than only in the narrative.
  5. [Audit Methods] The paper does not state the iOS version or Apple News version used in the simulator; since platform behavior can change between releases, including this information would improve reproducibility and comparability.
Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The audit does not introduce free parameters or invented entities. It relies on several domain assumptions about the platform and the validity of the measurement tools, the most important being that a simulator is a faithful proxy for real devices and that the section labels (algorithmic vs editorial) are accurate. These are external assumptions, not fitted from the data.

assumptions (4)
  • domain assumption The Appium iPhone simulator presents content identical to real iPhones for Apple News.
    All content-level results (localization, source diversity, topic n-grams) come from simulator data. The personalization experiment offers partial validation because 89.2% of real user screenshots matched the simulator control, but this does not establish full equivalence across all versions, accounts, or times.
  • domain assumption Trending Stories is algorithmically curated and Top Stories is editorially curated.
    Attributed to a New York Times report (Nicas, 2018) and Apple's public statements; the paper does not independently verify the provenance of the lists, and this attribution is the basis for comparing 'algorithmic logic' and 'editorial logic'.
  • domain assumption The n-gram log-ratio procedure identifies topic salience, and the resulting categories reflect 'soft news' and 'hard news'.
    The paper does not use an independent, validated classification of soft/hard news; the conclusion is based on inspecting the most salient bigrams and trigrams from each section (Table 1).
  • standard math The Hutcheson t-test assumptions are appropriate for comparing Shannon diversity indices between the two sections.
    The test is a standard method for comparing sample diversity. However, the stories are not independent random draws; they are time-series observations from a single channel, so the p-value should be interpreted cautiously.

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Pith. "Pith review of Auditing News Curation Systems: A Case Study Examining Algorithmic and Editorial Logic in Apple News." pith.science (2026). https://pith.science/paper/WPNE6OLW

@misc{pith2026190800456,
  author       = {Pith},
  title        = {Pith review of: Auditing News Curation Systems: A Case Study Examining Algorithmic and Editorial Logic in Apple News},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WPNE6OLW}},
  note         = {Machine review of arXiv:1908.00456}
}
read the original abstract

This work presents an audit study of Apple News as a sociotechnical news curation system that exercises gatekeeping power in the media. We examine the mechanisms behind Apple News as well as the content presented in the app, outlining the social, political, and economic implications of both aspects. We focus on the Trending Stories section, which is algorithmically curated, and the Top Stories section, which is human-curated. Results from a crowdsourced audit showed minimal content personalization in the Trending Stories section, and a sock-puppet audit showed no location-based content adaptation. Finally, we perform an extended two-month data collection to compare the human-curated Top Stories section with the algorithmically curated Trending Stories section. Within these two sections, human curation outperformed algorithmic curation in several measures of source diversity, concentration, and evenness. Furthermore, algorithmic curation featured more "soft news" about celebrities and entertainment, while editorial curation featured more news about policy and international events. To our knowledge, this study provides the first data-backed characterization of Apple News in the United States.

Figures

Figures reproduced from arXiv: 1908.00456 by the authors.

Figure 1
Figure 1. Two screenshots from the Apple News app taken [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. A depiction of our two audit methods: crowd [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 5
Figure 5. Relative distribution of Trending Stories over the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: Relative distribution of Top Stories over the hours [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 4
Figure 4. Figure 4: Relative distribution of Top Stories across top ten [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Reviewed August 14, 2026 · model on record in the stance chip above.