Understanding who talks about what: comparison between the information treatment in traditional media and online discussions

被引：2

作者：

Schawe, Hendrik ^{[1
]}

Beiro, Mariano G. ^{[2
,3
]}

Alvarez-Hamelin, J. Ignacio ^{[2
,3
]}

Kotzinos, Dimitris ^{[4
]}

Hernandez, Laura ^{[1
]}

机构：

[1] CY Cergy Paris Univ, CNRS, Lab Phys Theor & Modelisat, F-95300 Paris, France

[2] Univ Buenos Aires, Fac Ingn, Paseo Colon 850,C1063ACV, Buenos Aires, Argentina

[3] Univ Buenos Aires, CONICET, INTECIN, Paseo Colon 850,C1063ACV, Buenos Aires, Argentina

[4] CY Cergy Paris Univ, CNRS, ENSEA, ETIS UMR 8051, F-95300 Paris, France

来源：

SCIENTIFIC REPORTS | 2023年 / 13卷 / 01期

关键词：

TWITTER; AGENDA; ATTENTION; TIME;

D O I：

10.1038/s41598-023-30367-8

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We study the dynamics of interactions between a traditional medium, the New York Times journal, and its followers in Twitter, using a massive dataset. It consists of the metadata of the articles published by the journal during the first year of the COVID-19 pandemic, and the posts published in Twitter by a large set of followers of the @nytimes account along with those published by a set of followers of several other media of different kind. The dynamics of discussions held in Twitter by exclusive followers of a medium show a strong dependence on the medium they follow: the followers of @FoxNews show the highest similarity to each other and a strong differentiation of interests with the general group. Our results also reveal the difference in the attention payed to U.S. presidential elections by the journal and by its followers, and show that the topic related to the "Black Lives Matter" movement started in Twitter, and was addressed later by the journal.

引用

页数：15

共 64 条

[1] Analyzing mass media influence using natural language processing and time series analysis [J].