Inputs and results of "A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.'s case"

This repository contains the datasets and visualizations generated in our work: "A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.’s case".

Note: the data are all contained inside the data.zip file. You need to unzip the container to get access to all the files and directories listed below.

The data (citations) gathered accompanied by their annotated characteristics are stored in data/:

"cits_features.csv": a dataset containing all the entities (rows in the CSV) which have cited the Wakefield et al. retracted article, and a set of features characterizing each citing entity (columns in the CSV). The features included are: DOI ("doi"), year of publication ("year"), the title ("title"), the venue identifier ("source_id"), the title of the venue ("source_title"), yes/no value in case the entity is retracted as well ("retracted"), the subject area ("area"), the subject category ("category"), the sections of the in-text citations ("intext_citation.section"), the value of the reference pointer ("intext_citation.pointer"), the in-text citation function ("intext_citation.intent"), the in-text citation perceived sentiment ("intext_citation.sentiment"), and a yes/no value to denote whether the in-text citation context mentions the retraction of the cited entity    ("intext_citation.section.ret_mention").
Note: this dataset is licensed under a Creative Commons public domain dedication (CC0).

"cits_text.csv": this dataset stores the abstract ("abstract") and the in-text citations context ("intext_citation.context") for each citing entity identified using the DOI value ("doi").
Note: the data keep their original license (the one provided by their publisher). This dataset is provided in order to favor the reproducibility of the results obtained in our work.

Topic modeling

We run a topic modeling analysis on the textual features gathered (i.e. abstracts and citation contexts). The results are stored inside the topic_modeling/ directory. The topic modeling has been done using MITAO, a tool for mashing up automatic text analysis tools and creating a completely customizable visual workflow [1]. The topic modeling results for each textual feature are separated into two different folders, abstract/ for the abstracts, and intext_cit/ for the in-text citation contexts. Both the directories contain the datasets and visualizations generated using MITAO. 

References

[1] Ferri, P., Heibi, I., Pareschi, L., & Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. https://doi.org/10.19245/25.05.pij.5.2.3