My tweets bring all the traits to the yard: Predicting personality and
relational traits in Online Social Networks
- URL: http://arxiv.org/abs/2009.10802v1
- Date: Tue, 22 Sep 2020 20:30:56 GMT
- Title: My tweets bring all the traits to the yard: Predicting personality and
relational traits in Online Social Networks
- Authors: Dimitra Karanatsiou, Pavlos Sermpezis, Jon Gruda, Konstantinos
Kafetsios, Ilias Dimitriadis and Athena Vakali
- Abstract summary: This study aims to provide a prediction model for a holistic personality profiling in Online Social Networks (OSNs)
We first designed a feature engineering methodology that extracts a wide range of features from OSN accounts of users.
Then, we designed a machine learning model that predicts scores for the psychological traits of the users based on the extracted features.
- Score: 4.095574580512599
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Users in Online Social Networks (OSN) leaves traces that reflect their
personality characteristics. The study of these traces is important for a
number of fields, such as a social science, psychology, OSN, marketing, and
others. Despite a marked increase on research in personality prediction on
based on online behavior the focus has been heavily on individual personality
traits largely neglecting relational facets of personality. This study aims to
address this gap by providing a prediction model for a holistic personality
profiling in OSNs that included socio-relational traits (attachment
orientations) in combination with standard personality traits. Specifically, we
first designed a feature engineering methodology that extracts a wide range of
features (accounting for behavior, language, and emotions) from OSN accounts of
users. Then, we designed a machine learning model that predicts scores for the
psychological traits of the users based on the extracted features. The proposed
model architecture is inspired by characteristics embedded in psychological
theory, i.e, utilizing interrelations among personality facets, and leads to
increased accuracy in comparison with the state of the art approaches. To
demonstrate the usefulness of this approach, we applied our model to two
datasets, one of random OSN users and one of organizational leaders, and
compared their psychological profiles. Our findings demonstrate that the two
groups can be clearly separated by only using their psychological profiles,
which opens a promising direction for future research on OSN user
characterization and classification.
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