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

Abstract

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.

Delay model for the dynamics of information units in the digital environment

“…One of the classic research lines in sociophysics is the temporal dynamics of the public agenda, which involves tracking topics across various domains-economics, politics, culture-that emerge from processing large sets of articles published in the media (Pinto et al, 2019;Gozzi et al, 2020;Schawe et al, 2023). At the other end of the scale, the temporal consumption of individual words, such as X (ex Twitter) hashtags, has also been studied (Altmann et al, 2013;Lorenz-Spreen et al, 2019;Lin et al, 2021;Pardo Pintos et al, 2022).…”