As political polarization in the United States continues to rise, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diversity of individual perspectives typically leads to superior team performance on complex tasks, strong political perspectives have been associated with conflict, misinformation and a reluctance to engage with people and perspectives beyond one's echo chamber. It is unclear whether self-selected teams of politically diverse individuals will create higher or lower quality outcomes. In this paper, we explore the effect of team political composition on performance through analysis of millions of edits to Wikipedia's Political, Social Issues, and Science articles. We measure editors' political alignments by their contributions to conservative versus liberal articles. A survey of editors validates that those who primarily edit liberal articles identify more strongly with the Democratic party and those who edit conservative ones with the Republican party. Our analysis then reveals that polarized teams-those consisting of a balanced set of politically diverse editors-create articles of higher quality than politically homogeneous teams. The effect appears most strongly in Wikipedia's Political articles, but is also observed in Social Issues and even Science articles. Analysis of article "talk pages" reveals that politically polarized teams engage in longer, more constructive, competitive, and substantively focused but linguistically diverse debates than political moderates. More intense use of Wikipedia policies by politically diverse teams suggests institutional design principles to help unleash the power of politically polarized teams.
a b s t r a c tScience is a complex system. Building on Latour's actor network theory, we model published science as a dynamic hypergraph and explore how this fabric provides a substrate for future scientific discovery. Using millions of abstracts from MEDLINE, we show that the network distance between biomedical things (i.e., people, methods, diseases, chemicals) is surprisingly small. We then show how science moves from questions answered in one year to problems investigated in the next through a weighted random walk model. Our analysis reveals intriguing modal dispositions in the way biomedical science evolves: methods play a bridging role and things of one type connect through things of another. This has the methodological implication that adding more node types to network models of science and other creative domains will likely lead to a superlinear increase in prediction and understanding.
To date, social network analysis has been largely focused on pairwise interactions. The study of higher-order interactions, via a hypergraph network, brings in new insights. We study community detection in a hypergraph network. A popular approach is to project the hypergraph to a graph and then apply community detection methods for graph networks, but we show that this approach may cause unwanted information loss. We propose a new method for community detection that operates directly on the hypergraph. At the heart of our method is a regularized higher-order orthogonal iteration (reg-HOOI) algorithm that computes an approximate low-rank decomposition of the network adjacency tensor. Compared with existing tensor decomposition methods such as HOSVD and vanilla HOOI, reg-HOOI yields better performance, especially when the hypergraph is sparse. Given the output of tensor decomposition, we then generalize the community detection method SCORE [21] from graph networks to hypergraph networks. We call our new method Tensor-SCORE.In theory, we introduce a degree-corrected block model for hypergraphs (hDCBM), and show that Tensor-SCORE yields consistent community detection for a wide range of network sparsity and degree heterogeneity. As a byproduct, we derive the rates of convergence on estimating the principal subspace by reg-HOOI, with different initializations, including the two new initialization methods we propose, a diagonal-removed HOSVD and a randomized graph projection.We apply our method to several real hypergraph networks which yields encouraging results. It suggests that exploring higher-order interactions provides additional information not seen in graph representations.
Passionate disagreements about climate change, stem cell research, and evolution raise concerns that science has become a new battlefield in the culture wars. We used data derived from millions of online co-purchases as a behavioral indicator for whether shared interest in science bridges political differences or selective attention reinforces existing divisions. Findings reveal partisan preferences both within and across scientific disciplines. Across fields, customers for liberal or "blue" political books prefer basic science (e.g., physics, astronomy, and zoology), while conservative or "red" customers prefer applied and commercial science (e.g., criminology, medicine and geophysics). Within disciplines, red books tend to be co-purchased with a narrower subset of science books on the periphery of the discipline. We conclude that the political left and right share an interest in science in general, but not science in particular. This underscores the need for research into remedies that can attenuate selective exposure to "convenient truth", renew the capacity for science to inform political debate and temper partisan passions. In its quest for an objective understanding of the world 1 , modern science has practiced two distinct forms of political neutrality: as an apolitical "separate sphere" detached from ideological debates, and as a "pubic sphere" relevant to political issues but with balanced political engagement that facilitates reasoned deliberation and deference to evidence 2-5. Recent surveys support the view that science contributes not only to human knowledge but also to social integration, both as a voice of reason and also as a shared value. Joint surveys conducted by the American Association for the Advancement of Science (AAAS) and the Pew Research Center in 2009 and 2014 found that science remains near the top in public rankings of professions, well above that of clergy, despite the prevalence of liberals among scientists 6-8. Although nearly twothirds of respondents question evolution, even those who see conflict with issues of personal
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