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Social Media and Political Polarization: A Panel Study of 36 Countries from 2014 to 2020

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The rise of social media has attracted scholarly attention to its role in shaping political polarization. However, most of existing studies focused on examining how social media polarizes individual persons in a single country and ignored its impacts on society as a whole in different social contexts. We conduct a cross-country study to explore the country-level interaction between the process of political polarization and the social context, which is shaped by social media adoption, organization, censorship, and false information. Using the data from V-Dem and other sources, we analyze a panel sample of 36 OECD countries from 2014 to 2020. The results show that political polarization is positively related to social media censorship and false information. The connection between two waves of polarization is weakened by social media adoption but strengthened by social media organization. Drawing on social identity theory, we synthesize the findings and propose two theoretical routes to explain the influence of social media on political polarization.

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  1. Being based on ratings of multiple country experts, evaluative indicators in V-Dem are faced with both systematic and random errors. The major systematic error refers to differential item functioning (DIF), where different standards are adopted for ratings by experts from different backgrounds. To mitigate DIF, V-Dem employ bride coders who rate multiple countries for many years, lateral coders who rate many countries for one year, and anchoring vignettes where country experts rate the same set of hypothetical cases without contextual information. They provide information about patterns of varying thresholds utilized by different coders when translating their perceptions of a given survey question into ordinal ratings. The information is incorporated into the models of Bayesian item response theory (IRT) to measure DIF. Assuming that country experts cannot make perfect assessment, IRT models aggregate their ordinal ratings into an interval-level estimate of a latent concept, which is adjusted according to DIF. IRT models are also used to reduce random errors stemming from variation in expert reliability. IRT models compute the degree to which an expert randomly disagrees with other experts when coding the same cases, and produce a reliability estimate for each expert. More reliable coders contribute more to the final estimation of the latent concept.

  2. Negative values are normal in the V-Dem dataset because they are country-year point estimates derived from the V-Dem measurement model, which converts ordinal ratings to interval-valued estimates. The model produces a probability distribution over country-year scores on a standardized interval scale (similar to a normal “Z” score, ranging from − 5 to 5). For most purposes, these point estimates are recommended for time series regression and other estimation strategies. For more details, please refer to Coppedge et al. (2021a).


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This research was supported by Tsinghua University Initiative Scientific Program (2023THZWJC10) and Tsinghua Lab Research Program on Computational Communication and Intelligent Media.

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Correspondence to Zikun Liu.

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Lu, J., Sun, M. & Liu, Z. Social Media and Political Polarization: A Panel Study of 36 Countries from 2014 to 2020. Soc Indic Res (2024).

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