一个量化两两之间影响概率的国会推特网络数据集。
A Congressional Twitter network dataset quantifying pairwise probability of influence.
作者信息
Fink Christian G, Omodt Nathan, Zinnecker Sydney, Sprint Gina
机构信息
Gonzaga University Physics Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA.
Gonzaga University Mechanical Engineering Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA.
出版信息
Data Brief. 2023 Aug 28;50:109521. doi: 10.1016/j.dib.2023.109521. eCollection 2023 Oct.
We present a social network dataset based on interactions between members of the 117 United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained "probabilities of influence" between all pairs of Congresspeople. Twitter's application programming interface (API) V2 was used to determine the number of times each member of Congress retweeted, quote tweeted, replied to, or mentioned other Congressional members, and the probability of influence was found by normalizing the summed influence by the number of tweets issued by each Congressperson. This network may be of particular interest to the study of information diffusion within social networks.
我们展示了一个基于2022年2月9日至2022年6月9日期间美国117届国会成员之间互动的社交网络数据集。该数据集采用有向加权网络的形式，其中边权重是通过实证获得的所有国会议员对之间的“影响概率”。推特的应用程序编程接口（API）V2被用于确定每位国会议员转发、引用转发、回复或提及其他国会议员的次数，并且通过将总影响力除以每位国会议员发布的推文数量进行归一化来得出影响概率。该网络可能对社交网络内信息传播的研究特别有意义。
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