  In this article, we present a methodology which takes as input a collection
of retracted articles, gathers the entities citing them, characterizes such
entities according to multiple dimensions (disciplines, year of publication,
sentiment, etc.), and applies a quantitative and qualitative analysis on the
collected values. The methodology is composed of four phases: (1) identifying,
retrieving, and extracting basic metadata of the entities which have cited a
retracted article, (2) extracting and labeling additional features based on the
textual content of the citing entities, (3) building a descriptive statistical
summary based on the collected data, and finally (4) running a topic modeling
analysis. The goal of the methodology is to generate data and visualizations
that help understanding possible behaviors related to retraction cases. We
present the methodology in a structured step-by-step form following its four
phases, discuss its limits and possible workarounds, and list the planned
future improvements.
