A Window to the World: Americans' Exposure to Political News From Foreign Media Outlets

Peterson, Erik; Damm, Emily

doi:10.31235/osf.io/er48b

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…In this step, the definitions are used to classify the specific information, for instance with coders looking at each URL and judging by using the given definitions. This can be done manually (e.g., using MTurk coders, Peterson et al, 2018 ), using ad hoc ML algorithms (e.g., supervised ML to categorise the topic of news articles, Peterson & Damm, 2019 ), or using already available third‐party ML algorithms (e.g., Google's Vision AI, Bosch et al, 2019 ). Manual coding can prompt the same errors as for survey data, that is, that different individuals coding the same raw data have different judgments or that coders systematically misinterpret and misclassify some information.…”

Section: The Tem: Metered Data From a Quality Perspectivementioning

confidence: 99%

When Survey Science Met Web Tracking: Presenting an Error Framework for Metered Data

Bosch¹,

Revilla²

2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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Metered data, also called web-tracking data, are generally collected from a sample of participants who willingly install or configure, onto their devices, technologies that track digital traces left when people go online (e.g., URLs visited). Since metered data allow for the observation of online behaviours unobtrusively, it has been proposed as a useful tool to understand what people do online and what impacts this might have on online and offline phenomena. It is crucial, nevertheless, to understand its limitations. Although some research have explored the potential errors of metered data, a systematic categorisation and conceptualisation of these errors are missing. Inspired by the Total Survey Error, we present a Total Error framework for digital traces collected with Meters (TEM). The TEM framework (1) describes the data generation and the analysis process for metered data and (2) documents the sources of bias and variance that may arise in each step of this process. Using a case study we also show how the TEM can be applied in real life to identify, quantify and reduce metered data errors. Results suggest that metered data might indeed be affected by the error sources identified in our framework and, to some extent, biased. This framework can help improve the quality of both stand-alone metered data research projects, as well asThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: The Tem: Metered Data From a Quality Perspectivementioning

confidence: 99%

When Survey Science Met Web Tracking: Presenting an Error Framework for Metered Data

Bosch¹,

Revilla²

2022

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

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Improving the Quality of Individual-Level Web Tracking: Challenges of Existing Approaches and Introduction of a New Content and Long-Tail Sensitive Academic Solution

Adam,

Makhortykh,

Maier

et al. 2024

Social Science Computer Review

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This article evaluates the quality of data collection in individual-level desktop web tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard for the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic web tracking solution, WebTrack, an open-source tracking tool maintained by a major European research institution, GESIS. The design logic, the interfaces, and the backend requirements for WebTrack are discussed, followed by a detailed examination of the strengths and weaknesses of the tool. Finally, using data from 1,185 participants, the article empirically illustrates how an improvement in data collection through WebTrack leads to innovative shifts in the use of tracking data. As WebTrack allows for collecting the content people are exposed to beyond the classical news platforms, it can greatly improve the detection of politics-related information consumption in tracking data through automated content analysis compared to traditional approaches that rely on the source-level analysis.

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Investigating Older Adults’ Perceptions of AI Tools for Medication Decisions: Vignette-Based Experimental Survey (Preprint)

Vordenberg,

Nichols,

Marshall

et al. 2024

Preprint

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BACKGROUND Given the public release of large language models, research is needed to explore whether older adults would be receptive to personalized medication advice given by artificial intelligence (AI) tools. OBJECTIVE This study aims to identify predictors of the likelihood of older adults stopping a medication and the influence of the source of the information. METHODS We conducted a web-based experimental survey in which US participants aged ≥65 years were asked to report their likelihood of stopping a medication based on the source of information using a 6-point Likert scale (scale anchors: 1=not at all likely; 6=extremely likely). In total, 3 medications were presented in a randomized order: aspirin (risk of bleeding), ranitidine (cancer-causing chemical), or simvastatin (lack of benefit with age). In total, 5 sources of information were presented: primary care provider (PCP), pharmacist, AI that connects with the electronic health record (EHR) and provides advice to the PCP (“EHR-PCP”), AI with EHR access that directly provides advice (“EHR-Direct”), and AI that asks questions to provide advice (“Questions-Direct”) directly. We calculated descriptive statistics to identify participants who were extremely likely (score 6) to stop the medication and used logistic regression to identify demographic predictors of being likely (scores 4-6) as opposed to unlikely (scores 1-3) to stop a medication. RESULTS Older adults (n=1245) reported being extremely likely to stop a medication based on a PCP’s recommendation (n=748, 60.1% [aspirin] to n=858, 68.9% [ranitidine]) compared to a pharmacist (n=227, 18.2% [simvastatin] to n=361, 29% [ranitidine]). They were infrequently extremely likely to stop a medication when recommended by AI (EHR-PCP: n=182, 14.6% [aspirin] to n=289, 23.2% [ranitidine]; EHR-Direct: n=118, 9.5% [simvastatin] to n=212, 17% [ranitidine]; Questions-Direct: n=121, 9.7% [aspirin] to n=204, 16.4% [ranitidine]). In adjusted analyses, characteristics that increased the likelihood of following an AI recommendation included being Black or African American as compared to White (Questions-Direct: odds ratio [OR] 1.28, 95% CI 1.06-1.54 to EHR-PCP: OR 1.42, 95% CI 1.17-1.73), having higher self-reported health (EHR-PCP: OR 1.09, 95% CI 1.01-1.18 to EHR-Direct: OR 1.13 95%, CI 1.05-1.23), having higher confidence in using an EHR (Questions-Direct: OR 1.36, 95% CI 1.16-1.58 to EHR-PCP: OR 1.55, 95% CI 1.33-1.80), and having higher confidence using apps (EHR-Direct: OR 1.38, 95% CI 1.18-1.62 to EHR-PCP: OR 1.49, 95% CI 1.27-1.74). Older adults with higher health literacy were less likely to stop a medication when recommended by AI (EHR-PCP: OR 0.81, 95% CI 0.75-0.88 to EHR-Direct: OR 0.85, 95% CI 0.78-0.92). CONCLUSIONS Older adults have reservations about following an AI recommendation to stop a medication. However, individuals who are Black or African American, have higher self-reported health, or have higher confidence in using an EHR or apps may be receptive to AI-based medication recommendations.

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A Window to the World: Americans' Exposure to Political News From Foreign Media Outlets

Cited by 3 publications

References 18 publications

When Survey Science Met Web Tracking: Presenting an Error Framework for Metered Data

When Survey Science Met Web Tracking: Presenting an Error Framework for Metered Data

Improving the Quality of Individual-Level Web Tracking: Challenges of Existing Approaches and Introduction of a New Content and Long-Tail Sensitive Academic Solution

Investigating Older Adults’ Perceptions of AI Tools for Medication Decisions: Vignette-Based Experimental Survey (Preprint)

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