How similar are politicians to those who vote for them? This is a critical
question at the heart of democratic representation and particularly relevant at
times when political dissatisfaction and populism are on the rise. To answer
this question we compare the online discourse of elected politicians and their
constituents. We collect a two and a half years (September 2020 - February
2023) constituency-level dataset for USA and UK that includes: (i) the Twitter
timelines (5.6 Million tweets) of elected political representatives (595 UK
Members of Parliament and 433 USA Representatives), (ii) the Nextdoor posts
(21.8 Million posts) of the constituency (98.4% USA and 91.5% UK
constituencies). We find that elected politicians tend to be equally similar to
their constituents in terms of content and style regardless of whether a
constituency elects a right or left-wing politician. The size of the electoral
victory and the level of income of a constituency shows a nuanced picture. The
narrower the electoral victory, the more similar the style and the more
dissimilar the content is. The lower the income of a constituency, the more
similar the content is. In terms of style, poorer constituencies tend to have a
more similar sentiment and more dissimilar psychological text traits (i.e.
measured with LIWC categories).

选举政客和选民们在网上言论中的相似程度与选区的政治倾向无关，但选区选举结果和收入水平与语言风格及内容有相关性。

当选的政客和他们的选民有多相似？来自在线社交网络的定量证据

How Similar Are Elected Politicians and Their Constituents? Quantitative  Evidence From Online Social Network

Determining the similarities and differences between humans and artificial
intelligence is an important goal both in machine learning and cognitive
neuroscience. However, similarities in representations only inform us about the
degree of alignment, not the factors that determine it. Drawing upon recent
developments in cognitive science, we propose a generic framework for yielding
comparable representations in humans and deep neural networks (DNN). Applying
this framework to humans and a DNN model of natural images revealed a
low-dimensional DNN embedding of both visual and semantic dimensions. In
contrast to humans, DNNs exhibited a clear dominance of visual over semantic
features, indicating divergent strategies for representing images. While
in-silico experiments showed seemingly-consistent interpretability of DNN
dimensions, a direct comparison between human and DNN representations revealed
substantial differences in how they process images. By making representations
directly comparable, our results reveal important challenges for
representational alignment, offering a means for improving their comparability.

确定人类和人工智能之间的相似性和差异是机器学习和认知神经科学的重要目标。通过借鉴认知科学的最新发展，我们提出了一个通用框架，可在人类和深度神经网络 (DNN) 中产生可比较的表示。将此框架应用于人类和自然图像的 DNN 模型，揭示了一个低维度的 DNN 嵌入，包括视觉和语义维度。与人类不同，DNN 显示出视觉特征明显优于语义特征的优势，表明表示图像的策略存在分歧。尽管在硅中的实验显示 DNN 维度具有似乎一致的可解释性，但人类和 DNN 表示之间的直接比较表明它们在处理图像时存在重大差异。通过使表示直接可比较，我们的结果揭示了表示对齐的重要挑战，为提高它们的可比性提供了一种方法。

深度神经网络与人类表征对齐的基本维度

Dimensions underlying the representational alignment of deep neural  networks with humans

We present a general framework for comparing multiple groups of documents. A
bipartite graph model is proposed where document groups are represented as one
node set and the comparison criteria are represented as the other node set.
Using this model, we present basic algorithms to extract insights into
similarities and differences among the document groups. Finally, we demonstrate
the versatility of our framework through an analysis of NSF funding programs
for basic research.

本文提出了一个比较多组文档的通用框架， 使用一个二分图模型将文档组表示为一个节点集，比较标准表示为另一个节点集，通过该模型，提出了基本算法，以提取不同文档组之间的相似性和差异性，并通过对 NSF 基础研究项目的分析证明了该框架的多功能性。