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德国推特社交网络的连接性:调查信息传播现象的试验台。

The connectivity network underlying the German's Twittersphere: a testbed for investigating information spreading phenomena.

机构信息

Simula Research Laboratory, High Performance Computing, 1364, Fornebu, Norway.

Technical University of Berlin, Distributed and Operating Systems, 10623, Berlin, Germany.

出版信息

Sci Rep. 2022 Mar 8;12(1):4085. doi: 10.1038/s41598-022-07961-3.

Abstract

Online social networks are ubiquitous, have billions of users, and produce large amounts of data. While platforms like Reddit are based on a forum-like organization where users gather around topics, Facebook and Twitter implement a concept in which individuals represent the primary entity of interest. This makes them natural testbeds for exploring individual behavior in large social networks. Underlying these individual-based platforms is a network whose "friend" or "follower" edges are of binary nature only and therefore do not necessarily reflect the level of acquaintance between pairs of users. In this paper,we present the network of acquaintance "strengths" underlying the German Twittersphere. To that end, we make use of the full non-verbal information contained in tweet-retweet actions to uncover the graph of social acquaintances among users, beyond pure binary edges. The social connectivity between pairs of users is weighted by keeping track of the frequency of shared content and the time elapsed between publication and sharing. Moreover, we also present a preliminary topological analysis of the German Twitter network. Finally, making the data describing the weighted German Twitter network of acquaintances, we discuss how to apply this framework as a ground basis for investigating spreading phenomena of particular contents.

摘要

在线社交网络无处不在,拥有数十亿用户,产生大量数据。虽然像 Reddit 这样的平台基于论坛式组织,用户围绕主题聚集,但 Facebook 和 Twitter 则采用了以个人为主要关注对象的概念。这使得它们成为探索大型社交网络中个人行为的自然测试平台。在这些基于个人的平台之下是一个网络,其“朋友”或“关注者”边仅具有二元性质,因此不一定反映用户对之间的熟悉程度。在本文中,我们提出了德国 Twittersphere 背后的熟人“强度”网络。为此,我们利用推文中包含的全部非语言信息来揭示用户之间的社交熟人图,而不仅仅是纯粹的二元边。用户对之间的社交连接性通过跟踪共享内容的频率和发布与共享之间的时间间隔来加权。此外,我们还对德国 Twitter 网络进行了初步的拓扑分析。最后,我们讨论了如何应用这个框架作为调查特定内容传播现象的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd28/8904848/a65f453b4cca/41598_2022_7961_Fig1_HTML.jpg

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