A calibrated measure to compare fluctuations of different entities across timescales
Hołyst, Janusz A
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AbstractA 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 <jats:italic>reactivity</jats:italic><jats:italic>RA</jats:italic> 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.
CitationChołoniewski, J., Sienkiewicz, J., Dretnik, N., Leban, G., Thelwall, M. and Hołyst, J.A. (2020) A calibrated measure to compare fluctuations of different entities across timescales. Scientific Reports 10, 20673. https://doi.org/10.1038/s41598-020-77660-4
PublisherSpringer Science and Business Media LLC
Description© 2020 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1038/s41598-020-77660-4
SponsorsThe work was partially supported as RENOIR Project by the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie Grant Agreement No. 691152 and by Ministry of Science and Higher Education (Poland), Grant Nos. 34/H2020/2016, 329025/PnH/2016 and by National Science Centre, Poland Grant No. 2015/19/B/ST6/02612. J.A.H. was partially supported by the Russian Scientific Foundation, Agreement #17-71-30029 with co-financing of Bank Saint Petersburg and by POB Research Centre Cybersecurity and Data Science of Warsaw University of Technology within the Excellence Initiative Program—Research University (IDUB).
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/