Department of Network & Data Science, Central European University, Vienna, Austria.
Department of Mathematics, City St George's, University of London, London, UK.
Nat Commun. 2024 Nov 14;15(1):9560. doi: 10.1038/s41467-024-53868-0.
Existing studies of political polarization are often limited to a single country and one form of polarization, hindering a comprehensive understanding of the phenomenon. Here we investigate patterns of polarization online across nine countries (Canada, France, Germany, Italy, Poland, Spain, Turkey, UK, USA), focusing on the structure of political interaction networks, the use of toxic language targeting out-groups, and how these factors relate to user engagement. First, we show that political interaction networks are structurally polarized on Twitter (currently X). Second, we reveal that out-group interactions, defined by the network, are more toxic than in-group interactions, indicative of affective polarization. Third, we show that out-group interactions receive lower engagement than in-group interactions. Finally, we identify a common ally-enemy structure in political interactions, show that political mentions are more toxic than apolitical mentions, and highlight that interactions between politically engaged accounts are limited and rarely reciprocated. These results hold across countries and represent a step towards a stronger cross-country understanding of polarization.
现有关于政治极化的研究往往局限于单个国家和一种极化形式,这阻碍了对这一现象的全面理解。在这里,我们研究了九个国家(加拿大、法国、德国、意大利、波兰、西班牙、土耳其、英国和美国)的在线极化模式,重点关注政治互动网络的结构、针对外部群体的有毒语言的使用,以及这些因素与用户参与度的关系。首先,我们表明,Twitter(现为 X)上的政治互动网络在结构上存在极化。其次,我们揭示了网络定义的外部群体互动比内部群体互动更具毒性,表明存在情感极化。第三,我们表明外部群体互动的参与度低于内部群体互动。最后,我们在政治互动中发现了一种常见的盟友-敌人结构,表明政治提及比非政治提及更具毒性,并强调政治参与账户之间的互动是有限的,很少有相互回应。这些结果在各国都成立,代表着朝着更深入的跨国家理解极化迈出了一步。