American Behavioral Scientist, Ahead of Print.
The digital sphere and social media platforms have prompted new logics regarding information access and influence flows among media, politicians, and citizens. In this exploratory study, via a machine learning software and with data visualization methods, we analyzed social media data in order to find patterns that can contribute to comprehend the new dynamics of influence between the media, politicians, and citizenship in the context of social media and digital communication, specifically on Twitter. We analyzed who the top 50 Spanish generalist media with most followers started following in 2017, 2018, and 2019 on Twitter, the quintessential informational network. To do so, we melded data visualization computational and manual methods. We used an artificial intelligence big data analysis software to visualize the network of media from Spain in order to identify the sample. Afterward, we extracted the top followed accounts by the sample and categorized them in types of accounts, institution/citizenship, country, number of followers, and gender, to proceed with the data visualization to identify trends and patterns. The results show that these media accounts started following mainly accounts that belonged to male politicians from Spain. We could also spot among the years of the study an inversely proportional trend from the media that went from following mainly institutions to following a majority of citizens, and to start following more accounts with a smaller number of followers every year. The tendency to follow accounts from Spain that belong to men grew or remained a majority among the years of the study.