In spite of the different biases that are known to affect studies based on on-line social networks, in terms of age, gender, residence location, social status, etc., the enormous amount of information they convey remains useful in particular to detect trends in the evolution of social opinion, at least restricted to the users of these platforms whose amount increases continuously. Moreover nowadays traditional broadcastingmedia, like radio or television, diffuse information or opinions selected from on-line social networks thus coupling this large but biased set of users with the general population.
Here we describe the works we have done based on massive data that we collected from online social networks and from other traditional media, like written press, (here The New York Times) which have also developed a numerical version and regularily make usage of online platforms.
- Evolution of the political landscape during an election.
We study the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on data obtained from the micro-blogging platform Twitter. We build a semantic network based on the hashtags
used by all the users following at least one of the main candidates. With this network
we can detect the topics that are discussed in the society. At a difference with most
studies of opinion on social media, we do not choose the topics a priori, they emerge
from the community structure of the semantic network instead. We assign to each
user a dynamical topic vector which measures the evolution of her/his opinion in this
space and allows us to monitor the similarities and differences among groups of
supporters of different candidates.
Our results show that the method is able to detect the dynamics of opinion formation about different topics and, in particular, it can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of 2015 election.
- Comparing the dynamics of a traditional vs an online media.
In this work 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 rst 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 di erent 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 di erentiation of interests with the general group. Our results also reveal the di erence 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.
- Relationship between the source of information and political orientation. (Large public article in French, based on massive data collected during the last presidential elections in France.
We have collected, starting September 2021 the tweets posted by about 11M Twitter users who followed at least one of the candidates to the presidential election 2022, in France and/or one of the most important media of the country (radio, TV, press agencies, internet channels). We have kept those who were either geolocalized in France or have included in their profile some hint that they live in France (like the name of a french city or region). We have then classified according to the candidate they support, and according to the media they prefer. To do so we consider a user as a supporter of a candidate if more than 75% of his/her retweets correspond to that candidate. The same has been done for the media they prefer. Our results show a strong correlation between the supporters of the candidates and their media preferences. This correlation is stronger for supporters of extreme right parties than for those of leftists parties.
