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dc.contributor.author
Simari, Gerardo
dc.contributor.author
Martinez, Maria Vanina
dc.contributor.author
Gallo, Fabio Rafael
dc.contributor.author
Falappa, Marcelo Alejandro
dc.date.available
2022-10-21T11:22:21Z
dc.date.issued
2021-09
dc.identifier.citation
Simari, Gerardo; Martinez, Maria Vanina; Gallo, Fabio Rafael; Falappa, Marcelo Alejandro; The Big-2/ROSe Model of Online Personality: Towards a Lightweight Set of Markers for Characterizing the Behavior of Social Platform Denizens; Springer; Cognitive Computation; 13; 5; 9-2021; 1198-1214
dc.identifier.issn
1866-9964
dc.identifier.uri
http://hdl.handle.net/11336/174289
dc.description.abstract
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. 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. 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 it is 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. 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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BEHAVIOR PREDICTION
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COGNITIVE MODELS
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ETHICAL AI
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EXPLAINABILITY AND INTERPRETABILITY
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MACHINE LEARNING
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ONLINE PERSONALITY
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PERSONALITY MODELS
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
The Big-2/ROSe Model of Online Personality: Towards a Lightweight Set of Markers for Characterizing the Behavior of Social Platform Denizens
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-22T16:15:38Z
dc.journal.volume
13
dc.journal.number
5
dc.journal.pagination
1198-1214
dc.journal.pais
Alemania
dc.description.fil
Fil: Simari, Gerardo. Arizona State University; Estados Unidos. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
dc.description.fil
Fil: Gallo, Fabio Rafael. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Falappa, Marcelo Alejandro. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Cognitive Computation
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s12559-021-09866-1
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s12559-021-09866-1
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