Dipartimento di Informatica

In collaboration with Prof. Jussara Almeida and Prof. Marcos Goncalves (UFMG-Brazil), English speaking is mandatory to interact with them.

Topic 1: Academic research is strongly based on citations and references of solid sources. Studying anomalies in citations is therefore important to understand the quality of scientific research. This requires datasets containing information about papers and articles they cite. This thesis aims at building such a dataset for Computer Science research. As multiple efforts have been made for building such a corpus, the student will first search for open public datasets of articles and citations. The student will then evaluate the coverage and precision of such datasets. In case the public datasets are insufficient the student is expected to engineering a crawler to complement and update the dataset. The student is expected to deliver datasets that can be used to study research citations and eventual tools for extracting citations from articles and update existing data.

Main arguments (what you will learn): Web crawling, graph analysis, processing of large datasets.

Topic 2: This thesis will investigate the phenomenon of the "hyperprolific authors", who are the most productive (and anomalous) researchers according to a given repository in a specific period of time. Many of these authors are simply excellent researchers, others are the head of very large and strong research groups. Unfortunately, some of these cases are artificial: authors that inflate their profiles by sending vast numbers of papers to fast journals with bad reviewing reputations. In this thesis you will characterize these aspects, delivering an analysis of the key features of these hyperprolific authors.

Related reading: https://link.springer.com/article/10.1007/s11192-023-04676-8
Main arguments (what you will learn): Graph analysis, processing of large datasets, anomaly detection.