Giulio Giacomo Cantone
Bachelor in Economics, Thesis on: Fiscal policies in UK and France between 1756 and 1816.
Master Degree in Sociology, Thesis on: Evaluation Research and Recommender Systems
Work experiences in:
- Executive Clerk for Confindustria (Industry 4.0 - Technological Transition) [1.5 years]
- Secretary of an Electronic Sports (competitive video games) national club with 1000+ members [2 years mandate]
- Internships + projects in survey agencies
- Tutor and Trainer in Statistics & Data Science projects in 2 High Schools.
Has been a speaker/guest in many local, national and international conferences and large meetings on topics as innovation and new businesses.
Worth to mention Campus Party 2018 Milan, Campus Party 2019 Milan and Riot Games 10th Year Celebration Event in London.
Cantone's main research interests revolve around the so-called 'gaming' of evaluative social systems, which is a broad spectrum of phenomena ranging from the study of the adaptive behavioral processes in compliance of the (often implicit) introduction of standards in human activities to proper cases of online deception (e.g. fake accounts on Twitter). The theoretical framework, while being rooted in classical sociological thought (e.g. theory of deviance in Merton) do not follow a paradigm and is receptive of insights from different fields as Statistics, Game Theory and Network Science.
To investigate how systems are duped are employed methods falling into the category of Computational Social Science. For example, large datasets of digital traces are collected through scraping techniques and APIs.
The aim of this kind of research is to fuel the technological and methodological development of fraud detection techniques (e.g. Benford's Law) and to understand better how to system-design evaluation practices.
Articoli in Rivista:
Tomaselli, V., Battiato, S., Ortis, A., Cantone, G. G., Urso, S., & Polosa, R. (2021). Methods, Developments, and Technological Innovations for Population Surveys. Social Science Computer Review, 0894439321994218. https://doi.org/10.1177/0894439321994218
Tomaselli, V., & Cantone, G. G. (2020). Evaluating Rank-Coherence of Crowd Rating in Customer Satisfaction. Social Indicators Research. https://doi.org/10.1007/s11205-020-02581-8
Contributi in volume:
Tomaselli, V., & Cantone, G. G. (2020). Big Data e scale di rating: Un modello CUBE per l’analisi delle valutazioni del consumatore. In R. G. D’Agata, S. Gozzo, C. Pennisi, V. Asero, & R. Sampugnaro (Eds.), Big Data e processi decisionali. Strumenti per l’analisi delle decisioni giuridiche, politiche, economiche e sociali. (pp. 191–210). EGEA.
Tomaselli, V., & Cantone, G. G. (2020). A Preference Index Design for Big Data. In P. Mariani & M. Zenga (Eds.), Data Science and Social Research II. Methods, Technologies and Applications (pp. 343–351). Springer.
Tomaselli, V., & Cantone, G. G. (2020). An index for crowdsourced data on multipoint scales in tourism services evaluation. In A. D’Ambra, P. Amenta, A. Lucadamo, & A. Crisci (Eds.), Statistical Methods for Service Quality Evaluation (pp. 159–165). Pearson.
Tomaselli, V., & Cantone, G. G. (2020). Evaluating a Hybrid One-Staged Snowball Sampling through Bootstrap Method on a Simulated Population. In A. Pollice, N. Salvati, & F. Schirripa Spagnolo (Eds.), Book of short Papers SIS 2020 (pp. 1284–1289). Pearson.
Pre-prints:
Tomaselli, V., Cantone, G. G., & Mazzeo, V. (2021). The polarising effect of Review Bomb. ArXiv:2104.01140 [Cs, Stat]. http://arxiv.org/abs/2104.01140