INVESTIGADORES
FALAPPA Marcelo Alejandro
artículos
Título:
The Big-2/ROSe Model of Online Personality
Autor/es:
GERARDO I. SIMARI; M. VANINA MARTÍNEZ; FABIO R. GALLO; MARCELO A. FALAPPA
Revista:
Cognitive Computation
Editorial:
Springer
Referencias:
Lugar: New York; Año: 2021 vol. 13 p. 1198 - 1214
ISSN:
1866-9956
Resumen:
The Big-5/OCEAN personality traits model, one of the central approaches to psychometrics, has been shown to have many applications over a variety of disciplines. In particular, correlations have been studied leading to effective characterization of people´s behavior, and the model has become notorious for its role in the Cambridge Analytica/Facebook scandal surrounding the 2016 US presidential elections. Methods: In this paper, we develop Big-2 (or ROSe, for Relationship to Others and to Self), a model via which the personality of users of online platforms can be studied using a lightweight set of markers focused on online behavior, avoiding the major data privacy pitfalls afflicting approaches based on more powerful models that characterize personal aspects of the human psyche. Evaluation of Big-2´s effectiveness is done in two parts: a quantitative evaluation on a specific prediction task and a qualitative one based on an analysis of the different ways in which the Big-2 traits can be derived from online behavior, proposing a general template to guide such efforts. Results: Quantitative results show that our lightweight model can match or surpass the performance of Big-5 in a prediction task, while qualitative results show that itis feasible to implement the model based on the observation of basic online user behavior. Our main result is a general-purpose model that can be used to characterize the personality traits of users of online platforms in an ethical manner. Conclusions: Our proposed model provides a valuable tool to carry out effective and explainable analyses of online personality, avoiding the collection of unnecessary user data that would open the possibility for ethical violations.