I have recently started to teach a course in data analysis with R at the University of Vienna, and I am creating a free online book where I explain fundamental R functions and data analysis operations, with a specific focus on time series analysis.
I’ll update the online book as the course goes on, but some chapters are already online. You can read the book at this link: Time Series Analysis With R
Today we have released CooRnet, an R package developed for detecting coordinated link sharing behavior on Facebook and Instagram.
Given a list of URLs, the package queries the CrowdTangle API link endpoint and retrieves the Facebook shares performed by public pages, groups and verified accounts, identifying the networks involved in coordinated activity.
The basic functions of CooRnet are augmented with other useful functions that create, for instance, the graph of the coordinated networks (to do additional network analysis) or the dataset of the most shared URLs.
In these works we found that networks involved in coordinated link sharing behavior are consistently associated with the spread of misinformation on Facebook. In the two figures below you can see the proportion of blacklisted domains shared by coordinated and non-coordinated entities, and the proportion of problematic entities (signaled by Avaaz) included in the coordinated and non-coordinated entities, as emerged in our studies on the Italian elections.
Extending the computational method first introduced by Benkler, Faris, Roberts and others (see here and here), the paper makes use of Twitter data to measure partisan attention to news media sources in a multi-party political system.
To validate the method we compared our results with those obtained through a survey (ITANES), finding remarkable similarity (see figure below).
Furthermore, we analyzed the degree of polarization of the Italian online news media system we observed in the lead-up to the 2018 Italian election, finding a moderate level of polarization.
We also find that populist parties’ online communities relied on news sources characterized by an higher level of insularity (i.e. mainly shared on Twitter by their partisan community only) than non-populist ones.
Replication data and R code used in the study can be found here, while the paper can be read here.