  Social networks have triumphed in communicating people online, but they have
also been exploited to launch influence operations for manipulating society.
The deployment of software-controlled accounts (e.g., social bots) has proven
to be one of the most effective enablers for that purpose, and tools for their
detection have been developed and widely adopted. However, the way to analyze
these accounts and measure their impact is heterogeneous in the literature,
where each case study performs unique measurements. To unify these efforts, we
propose a common framework to analyze the interference of social bots in
Twitter. The methodology compares the non-authentic actors with the rest of
users from different perspectives, thus building objective metrics to measure
their actual impact. We validate the framework by applying it to a dataset of
Twitter iterations dated in the weeks preceding the 2019 Spanish general
election. In this sense, we check that our framework facilitates the
quantitative evaluation of unauthentic groups, particularly discovering that
social bots changed the natural dynamics of the network in these days, but did
not have a significant impact. We also consider this methodology as a practical
tool for the qualitative interpretation of experimental results, particularly
suggesting within the aforementioned electoral context that semi-automated
accounts are potentially more threatening than fully automated ones.
