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A calibrated measure to compare fluctuations of different entities across timescales.

ABSTRACT: A common way to learn about a system's properties is to analyze temporal fluctuations in associated variables. However, conclusions based on fluctuations from a single entity can be misleading when used without proper reference to other comparable entities or when examined only on one timescale. Here we introduce a method that uses predictions from a fluctuation scaling law as a benchmark for the observed standard deviations. Differences from the benchmark (residuals) are aggregated across multiple timescales using Principal Component Analysis to reduce data dimensionality. The first component score is a calibrated measure of fluctuations-the reactivity RA of a given entity. We apply our method to activity records from the media industry using data from the Event Registry news aggregator-over 32M articles on selected topics published by over 8000 news outlets. Our approach distinguishes between different news outlet reporting styles: high reactivity points to activity fluctuations larger than expected, reflecting a bursty reporting style, whereas low reactivity suggests a relatively stable reporting style. Combining our method with the political bias detector Media Bias/Fact Check we quantify the relative reporting styles for different topics of mainly US media sources grouped by political orientation. The results suggest that news outlets with a liberal bias tended to be the least reactive while conservative news outlets were the most reactive.

SUBMITTER: Choloniewski J

PROVIDER: S-EPMC7691371 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A calibrated measure to compare fluctuations of different entities across timescales.

Chołoniewski Jan J Sienkiewicz Julian J Dretnik Naum N Leban Gregor G Thelwall Mike M Hołyst Janusz A JA

Scientific reports 20201126 1

A common way to learn about a system's properties is to analyze temporal fluctuations in associated variables. However, conclusions based on fluctuations from a single entity can be misleading when used without proper reference to other comparable entities or when examined only on one timescale. Here we introduce a method that uses predictions from a fluctuation scaling law as a benchmark for the observed standard deviations. Differences from the benchmark (residuals) are aggregated across multi ...[more]

PMID: 33244096

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Dataset Information

A calibrated measure to compare fluctuations of different entities across timescales.

Publications

A calibrated measure to compare fluctuations of different entities across timescales.

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