| Additional Information: | Funding text
J.F., B.C. and B.K.S. were partially supported by DARPA-INCAS under Agreement No. HR001121C0165 and by NSF Grant No. BSE-2214216. H.A.M. was supported by NSF Grant No. BSE-2214217. Z.Z. was supported by the R&D Program of Beijing Municipal Education Commission, Grant No. KM202210038002. M.W.M. was partially supported by NSF Grant No. SES-2049207. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Supplementary Text
Definitions of Polarization
We acknowledge that political scientists distinguish multiple types of polarization [36, 37, 38,
39, 40, 41, 42, 43, 44]: affective polarization (the penchant for one partisan political group to
experience animus toward an opposing partisan group), policy polarization (extreme differences
of opinion on highly salient issues), partisan polarization (a substantive and affective division
based on identification with opposing political parties), ideological polarization (a substantive
and affective division based on identification with opposing ideological camps, e.g., liberals
versus conservatives), and geographic polarization (the regional alignment of opinions, e.g.,
“red state/blue state”). Furthermore, each of these five types of polarization can, in turn, be
classified by level: elite polarization among political officials and pundits, media polarization
among news organizations, and voter polarization among the underlying population as usually
measured by exit polls and opinion surveys. In the main manuscript, we seek to explore polarization
to quantify the various ways the Twitter communities disseminate news. Accordingly,
we opt to define polarization in the main manuscript as the growth in ideological separation
between Twitter users as characterized by the political alignment of the content they propagate.
Supplementary Information: Supplementary text, figs. 1–10 and tables 1–10.
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Flamino, James